The Role of Individual Differences in the Prediction of Cooperation, Deviance, and Performance by
Jan Luca Pletzer
A thesis submitted in partial fulfillment of the requirements for the degree of
Doctor of Philosophy in Business Administration
(Double Doctoral Agreement between Jacobs University and Vrije Universiteit Amsterdam)
Approved Dissertation Committee Prof. Dr. Sven C. Voelpel (Jacobs University Bremen) Prof. Dr. Adalbert F.X. Wilhelm (Jacobs University Bremen) Dr. Janneke K. Oostrom (Vrije Universiteit Amsterdam) Prof. Dr. D. Michael Kuhlman (University of Delaware)
Date of Defense: 21.03.2018 Department of Business and Economics
Statutory Declaration
Family Name, Given/First Name Pletzer, Jan Luca
Matriculation number 20331223 What kind of thesis are you submitting: PhD Thesis Bachelor-, Master- or PhD-Thesis
English: Declaration of Authorship
I hereby declare that the thesis submitted was created and written solely by myself without any external support. Any sources, direct or indirect, are marked as such. I am aware of the fact that the contents of the thesis in digital form may be revised with regard to usage of unauthorized aid as well as whether the whole or parts of it may be identified as plagiarism. I do agree my work to be entered into a database for it to be compared with existing sources, where it will remain in order to enable further comparisons with future theses. This does not grant any rights of reproduction and usage, however.
The Thesis has been written independently and has not been submitted at any other university for the conferral of a PhD degree; neither has the thesis been previously published in full.
German: Erklärung der Autorenschaft (Urheberschaft)
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Diese Arbeit wurde in der vorliegenden Form weder einer anderen Prüfungsbehörde vorgelegt noch wurde das Gesamtdokument bisher veröffentlicht.
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Date, Signature
2
3 TABLE OF CONTENTS
TABLE OF CONTENTS
TABLE OF CONTENTS ...... 4 ACKNOWLEDGMENTS ...... 6 SUMMARY ...... 9 CHAPTER 1 GENERAL INTRODUCTION ...... 14
INDIVIDUAL DIFFERENCES ...... 16
PERSONALITY ...... 17
PERSONALITY AS A PREDICTOR OF BEHAVIOR ...... 19
COOPERATION, DEVIANCE, AND PERFORMANCE ...... 20
OVERVIEW OF THE CHAPTERS ...... 21 CHAPTER 2 SOCIAL VALUE ORIENTATION, EXPECTATIONS, AND COOPERATION: A META-ANALYSIS ...... 24
ABSTRACT ...... 25
INTRODUCTION ...... 26
METHOD ...... 35
RESULTS ...... 47
DISCUSSION ...... 55
FOOTNOTES ...... 66 CHAPTER 3 SELFISHNESS FACILITATES DEVIANCE: THE LINK BETWEEN SOCIAL VALUE ORIENTATION AND DEVIANT BEHAVIOR ...... 69
ABSTRACT ...... 70
INTRODUCTION ...... 71
METHOD STUDY 1 ...... 77
RESULTS AND DISCUSSION STUDY 1 ...... 79
METHOD STUDY 2 ...... 81
RESULTS AND DISCUSSION STUDY 2 ...... 83
METHOD STUDY 3 ...... 87
RESULTS STUDY 3 ...... 88
META-ANALYSIS ON STUDY 1 THROUGH 3 ...... 90
GENERAL DISCUSSION ...... 90
FOOTNOTES ...... 96
4 TABLE OF CONTENTS
CHAPTER 4 PERSONALITY AND WORKPLACE DEVIANCE: A META-ANALYSIS ...... 97
ABSTRACT ...... 98
INTRODUCTION ...... 99
METHOD ...... 107
RESULTS ...... 120
DISCUSSION ...... 132
FOOTNOTES ...... 141 CHAPTER 5 AGE AND WORKPLACE DEVIANCE: A META-ANALYSIS ...... 144
ABSTRACT ...... 145
INTRODUCTION ...... 146
METHOD ...... 153
RESULTS ...... 166
DISCUSSION ...... 174 CHAPTER 6 DOES GENDER MATTER? FEMALE REPRESENTATION ON CORPORATE BOARDS AND FIRM FINANCIAL PERFORMANCE: A META-ANALYSIS ...... 182
ABSTRACT ...... 183
INTRODUCTION ...... 184
METHOD ...... 190
RESULTS ...... 196
DISCUSSION ...... 201 CHAPTER 7 GENERAL DISCUSSION ...... 212
OVERVIEW OF THE MAIN FINDINGS ...... 214
THEORETICAL CONTRIBUTIONS AND IMPLICATIONS ...... 217
PRACTICAL CONTRIBUTIONS AND IMPLICATIONS ...... 219
DIRECTIONS FOR FUTURE RESEARCH ...... 224
CONCLUDING REMARKS ...... 228 REFERENCES ...... 230 STATUTORY DECLARATION ...... ERROR! BOOKMARK NOT DEFINED.
5 ACKNOWLEDGEMENTS
ACKNOWLEDGMENTS The completion of this dissertation would not have been possible without the valuable help and support from multiple people. My deepest gratitude goes out to my supervisors who have worked with me on the chapters included in this dissertation during the past four years.
Without their continued support and advice, I would not have been able to finish this dissertation. I feel especially fortunate about the fact that all my supervisors did not just care about me as a PhD student, but also about me as a person. This made my PhD experience as enjoyable and great as it was and I will always look back with a smile on my face.
Sven, you always provided great guidance, cared about both my professional and personal life, and made the completion of this dissertation a fun, inspiring, and educational experience for me. Without you and your contagious enthusiasm for all projects we have worked on together, I would not have enjoyed the past four years as much as I have and I would not be where I am today. Janneke, thank you for being a great supervisor, for always providing detailed, informative, and quick feedback on all manuscript drafts, and for always having an open ear. You were always there when I had questions and taught me most of the things I know about science. It was a great pleasure to be your PhD student. Dan, a big thank you goes to you as well for writing our manuscript together and for providing guidance for all career decisions I made in the past years. Your meticulous approach to scientific work is something that I admire and still have to develop myself more. I have learned incredibly much from you and will cherish this experience. Paul, your knowledge, oversight, and experience is something that I have greatly profited from in the past years and that I look up to. You taught me to see the bigger picture and always provided valuable insights to improve this dissertation. I am really thankful that I was able to learn from all of you.
I also want to thank all faculty, staff, and fellow PhD students who were my colleagues during the past four years at Jacobs University Bremen and at the Vrije
Universiteit Amsterdam. There are too many to name, but please know that I am thankful for and have learned something from every single one of you. It was great to share this 6 ACKNOWLEDGEMENTS experience and to work with you, and I am sure our paths will cross again in the future. I am especially thankful that I was able to share most of my PhD experience with Fabiola. Thank you for always having an open ear and for being a friend at work. I also want to thank my office mates, Angelo and Nicolas, for answering all my questions, for making work an entertaining and fun experience, and for having interesting scientific (and political) discussions with me.
Another important individual for me during the past years was Mike Kuhlman. Thank you for welcoming me with open arms to the University of Delaware, for pushing me to think critically about an idea when I thought I had already done that, for the long discussions about behavior in social dilemmas (and about politics and life in general), and for introducing me to the world of science in the US.
In addition, I want to thank a few individuals without whom I would not even have thought about getting a PhD. Susanne Scheibe and Xavier Sanchez, you supervised me during the first steps of my scientific career and thereby sparked my interest in research. Thank you for that! I am also thankful to my cousin, Julia Quitmann, who was the first to propose to me that I should get a PhD. Without you, I would have never even tried to get a position as a PhD student and I am incredibly grateful to you for that.
Lastly, I want to thank everyone who was by my side during the last four years. I have the best family and friends I could ask for and I hope all of you know how grateful I am to have you in my life. You are the most important ones.
Jan
Amsterdam, 15.12.2017
7
8 SUMMARY
SUMMARY
9 SUMMARY
The expression of behavior is usually considered to be the product of situational characteristics and individual differences. While both are important in determining behavior, the present dissertation focuses on the utility of individual differences for explaining heterogeneity in behavior between individuals. Hence, the aim of the present dissertation is to contribute to a deeper understanding about the effects of individual differences on three outcomes crucial for social and organizational functioning – cooperation, deviance, and performance. Chapter 1 describes the use of individual differences in predicting these behaviors in more detail, explains the most important individual difference (i.e., personality) and its common conceptualization and measurement, and provides an overview of the remaining chapters included in this dissertation.
In Chapter 2, the relations between the narrow personality facet social value orientation (SVO), expectations of other’s cooperation, and cooperative behavior in social dilemmas are examined. More specifically, by meta-analytically integrating research from more than half a century, findings of this chapter demonstrate that individuals with social dispositions (i.e., prosocials) expect more cooperation from others in social dilemmas than individuals with selfish dispositions (i.e., individualists and competitors = proselfs).
Highlighting the importance of these expectations for the decision-making process, it is shown that expected partner cooperation partially mediates the relation between SVO and cooperation in social dilemmas. Importantly, expectations are positively related to cooperative behavior for prosocials and for proselfs, emphasizing a valuable opportunity to advance cooperative behavior between individuals and groups through eliciting the expectation that others are cooperating as well.
In Chapter 3, the focus shifts from using SVO to predict cooperative behavior in social dilemmas to using SVO to predict norm-violating deviant behavior. Across three studies that were conducted online and in the lab, proselfs consistently reported to behave more deviantly at work than prosocials. Importantly, these findings were corroborated by showing that proselfs also behaved more deviantly when deviant behavior was operationalized as the
10 SUMMARY disobedience to instructions or as the overrepresentation of own performance. These findings emphasize the selfish aspect underlying deviant behavior and highlight the usefulness of narrow personality facets in the prediction of deviant behavior.
Yet, broad personality domain scales are useful in predicting behavior as well, and in
Chapter 4 the relations between the two most common broad personality models (i.e., the Big
Five and the HEXACO) and workplace deviance are meta-analytically examined. Results based on 460 effect sizes indicate that HEXACO Honesty-Humility is the strongest predictor of workplace deviance out of all eleven broad personality domain scales that were examined.
Conscientiousness, Agreeableness, Neuroticism (for the Big Five) or Emotionality (for the
HEXACO), and Big Five Openness to Experience also significantly correlated with workplace deviance. Overall, the HEXACO personality model explained more variance in workplace deviance than the Big Five personality model, suggesting that researchers and practitioners might want to prioritize the HEXACO when aiming to predict levels of workplace deviance.
Building on the finding that personality strongly predicts levels of workplace deviance, Chapter 5 examines the relation between age and workplace deviance, and especially tests if personality and negative affect mediate this relationship. Age and workplace deviance correlate negatively with each other, and this relation is partially mediated by age-related changes in Conscientiousness, Agreeableness, and Neuroticism as hypothesized based on the neo-socioanalytical model of personality change. In addition, based on the socio-emotional selectivity theory we hypothesized that age-related decreases in experienced negative affect also mediate the relation between age and workplace deviance and found evidence of a partial mediation. As such, findings of this chapter for the first time test the mechanisms underlying the negative relation between age and workplace deviance.
The last empirical chapter of this dissertation examines how another important demographic characteristic – gender – relates to firm financial performance. More specifically, by meta-analytically summarizing all studies published on this relation, results
11 SUMMARY indicate that no relation between female representation on corporate boards and firm financial performance exists. Thus, findings did not provide evidence for a business case of diversity, which suggests that increased diversity results in performance benefits. However, the results suggest that organizations should prioritize females, and therefore increase gender diversity, in promotion decisions on corporate boards for ethical reasons when female candidates are equally qualified as male candidates.
Together, these five empirical chapters provide valuable new insights into the study of individual differences as predictors of behaviors that crucially determine social and organizational functioning. Chapter 7 summarizes the main findings, discusses theoretical and practical implications, and deduces directions for future research from the current findings. As such, this last chapter integrates findings from the five empirical chapters included in this dissertation.
12 SUMMARY
13 CHAPTER 1
CHAPTER 1 GENERAL INTRODUCTION
14 CHAPTER 1
15 GENERAL INTRODUCTION
When observing unexpected or deviant behavior, individuals have a tendency to immediately search for an explanation of this behavior. For example, when a colleague comes too late to work, two obvious reasons that readily come to mind are that this individual is someone who is late quite regularly or that external factors influenced this individual’s late arrival. Just like when it comes to being late, behavior in various areas of life is a product of individual differences and situational characteristics (Mischel & Shoda, 1995). Although situational characteristics are certainly important determinants of behavior, the present dissertation will focus on individual differences as predictors of behavior crucial for social and organizational functioning (i.e., cooperation, deviance, and performance). This introductory chapter will provide an overview of the overarching topic of this dissertation and will outline the purpose of the chapters included in this dissertation. First, individual differences and their importance will be briefly explained. Second, the most important individual difference, personality, will be defined and recent research on the conceptualization and measurement of personality will be presented. Third, research on the predictive validity of individual differences, and especially of personality, for cooperative and deviant behaviors will be reviewed. Fourth, the relationship between the three main outcome variables of this dissertation will be highlighted. The present chapter will conclude with a brief overview of the further chapters included in this dissertation.
Individual Differences
Whenever a certain behavior is assessed or measured, heterogeneity in responses between individuals exists. For example, if two individuals are asked how often they come too late to work, one might say ‘never’, whereas the other might say ‘at least once a week’.
Some of this heterogeneity in responses can be explained by differing characteristics of respondents, which are called individual differences. For the example above, these two individuals might differ on personality traits that determine punctuality, such as
Conscientiousness or Agreeableness (Back, Schmukle, & Egloff, 2006). A wide variety of scientific disciplines, such as medicine, economics, sociology, and especially psychology,
16 CHAPTER 1 utilize individual differences to predict behavior. In business administration and in organizational psychology, individual differences have received widespread attention in predicting behavior as well. For example, gender, age, or personality are used to predict various behaviors and behavioral outcomes, such as leadership behavior (Bono & Judge,
2004), commitment (Meyer, Stanley, Herscovitch, & Topolnytsky, 2002), organizational citizenship behaviors (Ng, Lam, & Feldman, 2016), or performance at work (Barrick &
Mount, 1991; O’Boyle, Humphrey, Pollack, Hawver, & Story, 2011; Tett, Jackson, Rohstein,
& Rothstein, 1991; Waldman & Avolio, 1986). Although all individual differences are potentially important and interesting, the most commonly studied individual difference in the prediction of behavior is personality.
Personality
While no universally accepted definition of personality exists, researchers generally agree that personality is a relatively stable trait. One definition describes personality as “the set of psychological traits and mechanisms within the individual that are organized and relatively enduring and that influence his or her interactions with, and adaptations to, the intrapsychic, physical, and social environments” (Larsen & Buss, 2005; p. 4). Modern trait- based personality research is largely build on the lexical approach, reflecting the idea that important personality characteristics are part of human language. Using this lexical approach, personality is described in terms of broad domain scales that comprise narrower facets, and it is most commonly assessed with the Big Five personality domain scales (Goldberg, 1982;
Goldberg, 1990; Gosling, Rentfrow, & Swann Jr, 2003; John & Srivastava, 1999). While some differences in the conceptualization and measurement of the Big Five exist between different approaches (i.e., Big Five by Goldberg, 1990, versus Five-Factor Model of
Personality by Digman, 1990), general scientific consensus exists that the five domain scales and their associated facets are (Soto & John, 2017):
• Openness to Experience: Intellectual Curiosity, Aesthetic Sensitivity, Creative
Imagination
17 GENERAL INTRODUCTION
• Conscientiousness: Organization, Productiveness, Responsibility
• Extraversion: Sociability, Assertiveness, Energy Level
• Agreeableness: Compassion, Respectfulness, Trust
• Neuroticism: Anxiety, Depression, Emotional Volatility
Although the Big Five domain scales represent the most common conceptualization of personality and have been the dominating personality model over the past decades, recent re- analyses of lexical data that have become available from more than ten different countries indicate that a six-factor structure of personality more accurately represents the human personality (Ashton, Lee, & De Vries, 2014; De Raad et al., 2014; Saucier, 2009). This new six-factor structure of personality has been termed the HEXACO model, and the six domain scales and associated facets are:
• Honesty-Humility: Sincerity, Fairness, Greed Avoidance, Modesty
• Emotionality: Fearfulness, Anxiety, Dependence, Sentimentality
• eXtraversion: Social Self-Esteem, Social Boldness, Sociability, Liveliness
• Agreeableness: Forgiveness, Gentleness, Flexibility, Patience
• Conscientiousness: Organization, Diligence, Perfectionism, Prudence
• Openness to Experience: Aesthetic Appreciation, Inquisitiveness, Creativity,
Unconventionality
While the domain scales of Extraversion, Conscientiousness, and Openness to
Experience are conceptually similar to their Big Five counterparts, the other three domain scales – Honesty-Humility, Emotionality, and Agreeableness – differ substantially from their
Big Five counterparts (please see Chapter 4 for an elaborate discussion of these differences).
Although the Big Five and the HEXACO are the most important and most often studied broad personality frameworks, other personality facets that are not directly part of these broad frameworks also exist and have been extensively studied. One such narrow personality facet is social value orientation (SVO), which describes the weights individuals attach to their own
18 CHAPTER 1 and to other’s outcomes in interdependent situations (McClintock, 1972; Van Lange, 1999), and which essentially measures someone’s cooperative preferences. While SVO can be measured continuously, individuals are commonly classified either as prosocials (who want to maximize equality or mutual outcomes), individualists (who want to maximize their own outcome in an absolute sense), or competitors (who want to maximize their own outcome in a relative sense). Mapping SVO onto the broader personality frameworks demonstrates that it shares significant overlap with HEXACO Honesty-Humility and with Big Five Agreeableness
(Hilbig, Glöckner, & Zettler, 2014).
Personality as a Predictor of Behavior
Independently of whether the Big Five or the HEXACO is being used, broad personality domain scales have proven useful in the prediction of various social and organizational behaviors, such as human cooperation (Hilbig, Thielmann, Klein, &
Henninger, 2016; Hilbig, Zettler, Leist, & Heydasch, 2013), leadership style (Bono & Judge,
2004), organizational citizenship behavior (Chiaburu, Oh, Berry, Li, & Gardner, 2011), and job performance (Barrick & Mount, 1991; Tett et al., 1991). However, only relying on broad personality domain scales might not always be optimal for several reasons. First, the criterion- related validity of narrow personality facets can be better (Ashton, Paunonen, & Lee, 2014;
Hastings & O’Neill, 2009; Pomerance & Converse, 2014). Second, the effects of individual facets in explaining a criterion might suppress each other (e.g., Hastings & O’Neill, 2009).
And third, there can be a higher conceptual resemblance between narrow facets and the predicted behavior (Ashton et al., 2014), which is especially important when the test-takers need to perceive that the test is relevant to the tested behavior (i.e., in job selection settings;
Hastings & O’Neill, 2009). To balance the benefits and limitations of the two approaches, the present dissertation will focus on the effects of both broad personality constructs (Big Five and HEXACO) and narrow personality facets (SVO) in predicting behavior. In addition, two important and universal demographic characteristics – age and gender – will be examined as predictors of behavior.
19 GENERAL INTRODUCTION
Cooperation, Deviance, and Performance
The effect of individual differences in personality and in these demographic characteristics on behaviors and behavioral outcomes crucial for social and organizational functioning will be examined. As such, this dissertation will focus on three important behaviors and behavioral outcomes – cooperation in interdependent situations, workplace deviance, and organizational performance. While these three constructs are not directly related at face value, they share significant overlap: the occurrence of workplace deviance can be conceptualized as the absence of cooperative behavior between employees or between employees and their organization. In addition, the occurrence of workplace deviance is defined as a violation of norms (Bennett & Robinson, 2000), whereas cooperative behavior is often a result of the enforcement of norms (Fehr, Fischbacher, & Gächter, 2002), and the absence of cooperative behavior (i.e., defection) usually violates social norms. Ultimately, organizational performance might be the result of the adherence to norms, of cooperative behavior between employees, and of the absence of deviant behavior at work.
Previous research has also shown that these three outcome variables are caused by similar antecedents. To explain why individuals cooperate with each other and do not act strictly according to their self-interest has been a major puzzle in a wide variety of scientific disciplines. Research has focused on situational characteristics that increase cooperation between individuals, such as punishment (Balliet, Mulder, & Van Lange, 2011), reciprocity
(Romano & Balliet, 2017), or reputation (Balliet, Wu, & De Dreu, 2014), but individual differences, such as gender (Balliet, Li, Macfarlan, & Van Vugt, 2011) and personality
(Hilbig et al., 2013; Koole, Jager, van den Berg, Vlek, & Hofstee, 2001; Volk, Thöni, &
Ruigrok, 2011; Zhao & Smillie, 2015) have also received widespread attention as predictors of human cooperation. Similarly, the occurrence of workplace deviance has been explained by using situational characteristics, such as abusive supervision (Tepper et al., 2009; Wang, Mao,
Wu, & Liu, 2012), stress (Chiu, Yeh, & Huang, 2015; Fox, Spector, & Miles, 2001), or perceptions of injustice (Berry, Ones, & Sackett, 2007; Henle, 2005; O’Neill, Lewis,
20 CHAPTER 1
Carswell, & O’Neill, 2011). However, individual differences, such as gender (Ng et al., 2016) and personality (Berry, Carpenter, & Barratt, 2012; Berry et al., 2007; Salgado, 2002), have also been examined as predictors of workplace deviance. Lastly, organizational performance is also the result of situational characteristics, such as politics and national governance
(Yoshikawa, Zhu, & Wang, 2014) or market pressures (Nickell, Nicolitsas, & Dryden, 1997).
Yet, similar to cooperation and workplace deviance, various individual differences have been used to predict organizational performance as well (Joecks, Pull, & Vetter, 2012; Peterson,
Smith, Martorana, & Owens, 2003; Van Ness, Miesing, & Kang, 2010).
The aim of the current dissertation is to use three important individual differences – personality, age, and gender – to contribute to the facilitation and prediction of human cooperation, to the prevention and prediction of workplace deviance in organizations, and lastly to the better understanding of the drivers of organizational performance. On the following pages, each chapter of this dissertation will be briefly outlined.
Overview of the Chapters
Chapter 2 examines how individual differences in the narrow personality facet SVO predict expectations of cooperation in social dilemmas. Specifically, differences in expectations have not been previously compared between prosocials, individualists, and competitors (i.e., the three primary SVOs), and these expectations about others’ behavior are fundamental building blocks of social cognition (Holmes, 2002). Hence, expectations determine behavior (Balliet & Van Lange, 2012), but it has not yet been examined if these expectations mediate the well-established relationship between SVO and cooperation in social dilemmas (Balliet, Parks, & Joireman, 2009), and if a possible mediation holds for both prosocials and proselfs (i.e., individualists and competitors). Results of this study will provide important insights for the study of human cooperation.
To connect the first chapter about the narrow personality facet SVO with the following chapters about the prediction of workplace deviance, Chapter 3 investigates the predictive validity of SVO for workplace deviance. The hypothesis that selfish individuals are
21 GENERAL INTRODUCTION more prone to act deviantly than prosocials is tested using three studies. In addition to contributing to the prediction and prevention of workplace deviance, the studies in this chapter will also examine the usefulness of relying on narrow personality facets as predictors of workplace deviance.
Whereas the third chapter examines the predictive validity of a narrow personality facet for workplace deviance, Chapter 4 investigates how and which broad personality domain scales explain and predict levels of workplace deviance. A few previous meta- analyses have already examined the relations between the Big Five personality dimensions and workplace deviance as a byproduct of larger investigations into the causes of workplace deviance (Berry et al., 2012, 2007; Salgado, 2002), but a comprehensive overview is still missing from the literature. In addition, the six-factor HEXACO personality model has received increased interest among researchers and practitioners in recent years but has not been meta-analytically examined as a predictor of workplace deviance. Therefore, this chapter will meta-analytically compare the Big Five and the HEXACO model in their predictive validity for workplace deviance. In addition, several methodologically, theoretically, and practically relevant moderators are examined.
Chapter 5 builds on the fourth chapter by investigating the relation between age and workplace deviance, and by examining which underlying age-related changes are responsible for the negative relation between age and workplace deviance. Based on socio-emotional selectivity theory (Carstensen, 1992), it is hypothesized that individuals experience less negative affect with increasing age, which subsequently is associated with decreased levels of workplace deviance. An additional hypothesis, which is based on the neo-socioanalytical model of personality change (Roberts & Wood, 2006), is that changes in personality across the lifespan can explain the negative relation between age and workplace deviance. As such, this paper integrates research on two individual differences in the prediction of workplace deviance, age and personality, and thereby extends findings from the fourth chapter.
22 CHAPTER 1
Chapter 6 examines how another individual difference, namely gender, predicts financial performance of organizations. More specifically, it is examined how the percentage of females on corporate boards relates to the financial performance of organizations. By meta- analytically integrating results from studies conducted in different countries, findings of this study will provide insights about the business case of (gender) diversity and introduce another important individual difference as a predictor of a crucial behavioral outcome – organizational performance.
Together, these five empirical chapters will advance both theory and practice by strengthening the understanding of individual differences as a predictor of cooperation, workplace deviance, and ultimately of organizational performance. The last chapter of this dissertation (Chapter 7) will summarize and integrate research findings from these five chapters and will discuss theoretical and practical implications, as well as limitations and ideas for future research.
23 SVO, EXPECTATIONS, AND COOPERATION
CHAPTER 2 SOCIAL VALUE ORIENTATION, EXPECTATIONS, AND COOPERATION: A META-ANALYSIS
This chapter is in press as Pletzer, J. L., Balliet, D. P., Joireman, J., Kuhlman, D. M., Voelpel, S. C., & Van Lange, P. A. M. (in press). Social value orientation, expectations, and cooperation: A meta-analysis. European Journal of Personality. A paper draft was presented at the 17th International Conference on Social Dilemmas 2017.
24 CHAPTER 2
Abstract
Interdependent situations are pervasive in human life. In these situations, it is essential to form expectations about the other’s behavior to adapt one’s own behavior to increase mutual outcomes and avoid exploitation. Social value orientation, which describes the dispositional weights individuals attach to their own and to another person’s outcome, predicts these expectations of cooperation in social dilemmas – an interdependent situation involving a conflict of interests. Yet, scientific evidence is inconclusive about the exact differences in expectations between prosocials, individualists, and competitors. The present meta-analytic results show that, relative to proselfs (individualists and competitors), prosocials expect more cooperation from others in social dilemmas, whereas individualists and competitors do not significantly differ in their expectations. The importance of these expectations in the decision process is further highlighted by the finding that they partially mediate the well-established relation between social value orientation and cooperative behavior in social dilemmas. In fact, even proselfs are more likely to cooperate when they expect their partner to cooperate.
Keywords: cooperation, social value orientation, expectations, trust, social dilemmas
25 SVO, EXPECTATIONS, AND COOPERATION
Introduction
Human cooperation is a topic that cuts across several scientific disciplines. The general goal is to understand the mechanisms supporting cooperation. An especially important scientific challenge involves understanding human cooperation in social dilemmas (i.e., situations in which short-term self-interest conflicts with long-term collective interests; Parks,
Joireman, & Van Lange, 2013; Van Lange, Joireman, Parks, & Van Dijk, 2013). Notably, many social dilemmas involve decision-makers with little to no information about the motives and likely actions of others – for example, in group projects with new colleagues. In these situations, the decision-maker’s dispositional concern for other’s welfare (or social value orientation (SVO); prosocial, individualistic, and competitive orientation; Van Lange, Otten,
De Bruin, & Joireman, 1997) and expectations about others’ choices affect cooperation. Yet, it is not clear whether or how these two key variables work together in promoting cooperation.
According to the goal-expectation hypothesis (Pruitt & Kimmel, 1977), cooperation requires both the goal of cooperating (i.e., a desire to maximize joint outcomes) and the expectation that one’s partner(s) will cooperate. In other words, SVO interacts with expectations to drive cooperation, such that only prosocials who expect others to cooperate will themselves cooperate (Boone, Declerck, & Kiyonari, 2010). An alternative possibility is that social motives influence expectations which in turn predict levels of cooperation.
Restated, expectations (at least partially) mediate the impact of SVO on cooperation. In their thorough review of the literature on SVO, expectations, and cooperation, Bogaert, Boone, and
Declerck (2008) offer an integrative model proposing that expectations serve to both moderate and mediate the impact of social motives on cooperation.
In the present paper, we utilize meta-analysis to test both the moderation and mediation models. While it is clear that cooperation in social dilemmas is reliably associated with differences in SVO (Balliet, Parks, & Joireman, 2009) and expectations (Balliet & Van
Lange, 2013), it is less clear how SVO and expectations work together to drive cooperation.
26 CHAPTER 2
Our meta-analysis offers four contributions to the work on SVO and cooperation in social dilemmas. First, we estimate if the three primary SVOs (i.e., prosocials, individualists, and competitors) differ in their expectations of partner cooperation. Previous research has been inconclusive regarding the exact magnitude of differences in expectations, especially when comparing individualists and competitors (e.g., Kuhlman & Wimberley, 1976; Van Lange,
1992). Moreover, studies always contain very few individuals who dispositionally pursue relative gains over others (i.e., competitors, about 12% of the population; Au & Kwong, 2004;
Van Lange et al., 1997), and a meta-analysis can provide a relatively high powered test whether competitors differ from the more common prosocials and individualists in their expectations of other’s cooperation. Second, we examine how variability across the studies affects the relation between SVO and expectations of other’s cooperation, such as group size, participant payment, and one-shot versus repeated interactions. Third, we harness recent developments in meta-analysis to provide the first meta-analytic test of the indirect effect of expectations on the relation between SVO and cooperation in social dilemmas. Fourth, we test the assertion that prosocials condition their cooperation on expected partner cooperation, but that individualists’ and competitors’ decisions to cooperate are independent of expected partner cooperation.
Social Value Orientation and Cooperation in Social Dilemmas
A long history of theoretical development and experimental research in the social and biological sciences has focused on understanding human cooperation in a situation when cooperation is difficult to achieve – social dilemmas (Van Lange et al., 2013). A social dilemma is an interdependent social interaction that contains a conflict between individual and collective interests (Dawes, 1980). In social dilemmas, individuals can achieve the best outcome by deciding not to cooperate while the partner does cooperate (temptation outcome
(T)). However, mutual cooperation (reward outcome (R)) always yields a larger outcome than mutual defection (punishment outcome (P)). The worst possible outcome occurs by cooperating with a partner who does not cooperate (sucker outcome (S)). The payoffs in all
27 SVO, EXPECTATIONS, AND COOPERATION social dilemmas follow the same basic structure: T > R > P > S, and all social dilemmas contain a clear structural incentive to defect.
The most widely studied personality construct in relation to cooperation in social dilemmas is SVO – defined in terms of the dispositional weights individuals assign to their own and to others’ outcomes in interdependent situations (Kuhlman, Camac, & Cunha, 1986;
McClintock, 1972). The SVO construct is derived from research on behavior in experimental games. Traditional game theory assumes that the decisions of individuals in interdependent situations are governed by a motivation to maximize own outcomes (e.g., Luce & Raiffa,
1957), and this assumption of “rational self-interest” has dominated much subsequent theory and research in various disciplines. Because research uncovered considerable individual variation in behavior in various economic games, researchers started to examine motives that transcend (short-term) self-interest. In particular, a guiding assumption underlying research on
SVO has been that some individuals consider not only their own outcome in interdependent situations, but also the outcomes of other individuals (Messick & McClintock, 1968) and value equality in outcomes (Van Lange, 1999). As such, SVO reflects stable individual differences in an inherent sense of fairness and equality in outcomes.1
Three SVOs are frequently distinguished in the population: (a) Prosocials aim to equalize and/or maximize joint outcomes; (b) Individualists aim to maximize their own outcomes, regardless of the other’s outcomes; and (c) Competitors aim to maximize the relative difference between their own and the other’s outcome. Individualists and competitors are often combined in a proself category (Liebrand, 1984; Van Lange & Kuhlman, 1994).
Over the past decades, SVO has usually been assessed with (1) the Triple Dominance
Measure (TDM; Van Lange et al., 1997) (2) the Ring Measure (Liebrand, 1984; Liebrand &
McClintock, 1988), and (3) the Slider Measure (Murphy, Ackermann, & Handgraaf, 2010).
Table 1 displays an example item from each of these SVO measures. Each measure has participants allocate points between themselves and another hypothetical individual.
Furthermore, participants are told that the other individual is making the same set of choices
28 CHAPTER 2 that affect the participant’s outcomes. For example, in the TDM, participants choose between three options: A) 500 points to the self, 500 points to the other (i.e., cooperative choice), B)
560 points to the self, 300 points to the other (i.e., individualistic choice), or C) 490 points to the self and 90 points to the other (i.e., competitive choice). In the TDM, participants are classified as either prosocials, individualists, or competitors if they make enough choices (six out of nine) consistent with one of the three SVOs. The Ring Measure, in turn, allows for a continuous and for a categorical assessment of SVO, but shows lower test-retest reliability compared to other measures (Liebrand, 1984). Finally, the recently developed SVO Slider
Measure overcomes the limitations of the TDM and the Ring Measure because it is efficient and easy to implement and shows good internal consistency while measuring SVO as a continuous construct, with higher scores indicating a more prosocial SVO (Murphy et al.,
2010). In this 6-item measure, participants are asked to choose between several self-other payoff combinations. Based on their decisions, an SVO angle on a two-dimensional space consisting of own payoff and other’s payoff can be computed. The Slider measure has good convergent validity with both the TDM and the Ring Measure (Murphy et al., 2010).2
SVO is a feature of personality as evidenced by its temporal stability (e.g., Van Lange,
Bekkers, Chirumbolo, & Leone, 2012) and its relation to several other relevant personality constructs. In fact, SVO shares significant overlap with HEXACO Honesty-Humility (and with Big Five Agreeableness; Hilbig, Glöckner, & Zettler, 2014). Honesty-Humility describes the tendency to be fair and honest (Ashton & Lee, 2007) and is associated with various socially desirable behaviors, such as a lower likelihood to sexually harass someone (Lee,
Gizzarone, & Ashton, 2003) or to be delinquent and criminal (De Vries & Van Gelder, 2013,
2015), and with increased interpersonal cooperation (Thielmann & Hilbig, 2014). Similarly, decades of research have shown that SVO reliably predicts cooperation not only in social dilemmas (Balliet et al., 2009), but also across a broad range of natural settings (e.g., Van
Lange, 2000; Van Lange, Van Vugt, Meertens, & Ruiter, 1998). For example, relative to individualists and competitors, prosocials tend to donate more to a variety of noble causes
29 SVO, EXPECTATIONS, AND COOPERATION
(e.g., McClintock & Allison, 1989; Van Andel, Tybur, & Van Lange, 2016), are more strongly involved in volunteering (e.g., Van Lange, Schippers, & Balliet, 2011), are more prone to exhibit citizenship behavior in organizations (e.g., Nauta, De Dreu, & Van Der
Vaart, 2002), and engage more often in pro-environmental behavior (e.g., Cameron, Brown,
& Chapman, 1998; Joireman, Lasane, Bennett, Richards, & Solaimani, 2001).
SVO and Expectations of Others’ Cooperation
In social dilemmas, one’s own choice and predispositions are often the basis of beliefs about the other’s behavior, especially in situations that lack information about the other individuals (Holmes, 2002; Krueger & Acevedo, 2007). The most widely studied personality characteristic used to predict expectations of other’s behavior in social dilemmas is SVO.
Beginning with the classic work of Kelley and Stahelski (1970), research focused on individual differences in cooperative behavior has shown that prosocials expect more cooperation from others in social dilemmas than proselfs (e.g., Messé & Sivacek, 1979; Van
Lange, 1990). Three models have been offered to explain how these dispositional preferences for cooperation influence expectations of other’s cooperative preferences. First, the triangle hypothesis proposes that previous experiences and self-fulfilling prophecies lead prosocials to expect heterogeneous behavior from others, whereas proselfs, through their own competitive behavior, elicit only competitive behavior in others and therefore expect only competitive behavior from others (Kelley & Stahelski, 1970; Van Lange, 1992).
30 CHAPTER 2
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a stage mixed stage 73 - , Van Lange, P. A. M., Otten, W., De Bruin, E. M., & E. Van Lange, P. M., A.De Bruin, W., M., Otten, Joireman,(1997). J. A. individualistic, Theory and orientations: and competitive preliminary evidence. Psychology 3514.73.4.733 M. Murphy, J. O., A., Handgraaf, K. J. R. Ackermann, & (2010). value social orientation. Measuring Making Decision Liebrand, W. B. G. (1984). The effect of social motives, Liebrand, W. Themotives, (1984). social G. of B. effect N communication on inand size group behaviour an person multi of Psychology Social doi:10.1002/ejsp.2420140302
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31 SVO, EXPECTATIONS, AND COOPERATION
Second, the Structural Assumed Similarity Bias (SASB) proposes that individuals with all SVOs project their own dispositions onto others and expect others to be similar to themselves (Kuhlman et al., 1986; Kuhlman & Wimberley, 1976; Ross, Greene, & House,
1977). Finally, the Cone Model only slightly differs from the SASB as it suggests that this false consensus effect is larger for individualists than for prosocials or competitors (Iedema &
Poppe, 1994b, 1999), possibly due to the overestimation of self-interest as a dominant motive underlying social behavior (Miller & Ratner, 1998; Vuolevi & Van Lange, 2010; Vuolevi &
Van Lange, 2012).
It is important to note that the current meta-analysis cannot test the three models against each other because the models make predictions about the social dynamics and psychological processes that give rise to the social projection of SVO, and not directly about expected cooperation in social dilemmas.3 However, it can be assumed that expectations about the distribution of SVO in the population correlate quite highly with expectations of other’s cooperation in social dilemmas. Hence, the underlying mechanism of self-fulfilling prophecies or social projection might drive differences in expectations, and subsequently cooperation, as well. Importantly, it needs to be stressed that all three accounts propose that expectations precede and determine cooperative behavior, which is supported by findings showing that dispositional, manipulated, and situation-specific trust all facilitate cooperative behavior (Boone et al., 2010; Kuhlman & Marshello, 1975). While these models diverge on the underlying mechanisms linking different SVOs to expectations in social dilemmas, they all also concur that SVO strongly determines expectations of cooperation, such that relatively more prosocially minded individuals should also expect more cooperation from others.
Despite this long-standing assumption, existing evidence is inconclusive about the exact magnitude of these differences in expected cooperation between prosocials, individualists, and competitors, pointing to the value of meta-analytically estimating these effects.
SVO, Expectations, and Cooperation: A Mediation Model
32 CHAPTER 2
In addition to meta-analyzing the effect of SVO on expectations, we were interested in determining whether expectations mediate the influence of SVO on cooperation. In fact, two prior meta-analyses point to that possibility, as cooperation in social dilemmas has been reliably linked with SVO (Balliet et al., 2009; Renkewitz, Fuchs, & Fiedler, 2011) and expectations (or trust) (Balliet & Van Lange, 2013), providing two pieces of evidence consistent with the mediation model. Also consistent with the mediation model, it has long been assumed that personality exerts its influence on behavior by affecting how people construe situations (e.g., Funder, 2009). This is especially true in situations where decision- makers lack information about their interaction partners (e.g., Holmes, 2002).
It is important to note that the expectation-cooperation link can be explained in two ways: 1) Individuals who exhibit cooperative behavior might justify their own behavior by expecting cooperation from others (self-justification; Dawes, McTavish, & Shaklee, 1977), or
2) individuals assume that others are similar to themselves and therefore expect cooperation, which leads them to cooperate (assumed similarity; Messé & Sivacek, 1979). However, scientific evidence and the three theoretical accounts mentioned before suggest that expectations precede and determine cooperative behavior (Boone et al., 2010; Iedema &
Poppe, 1994a, 1999; Kelley & Stahelski, 1970; Kuhlman et al., 1986; Kuhlman & Marshello,
1975; Kuhlman & Wimberley, 1976; Van Lange, 1992). In addition, if cooperative behavior would determine expectations (and not vice versa), the correlation between expectations and cooperation should be stronger when expectations are assessed after cooperation. However, a recent meta-analysis including 104 studies that measured expectations either before or after decisions of cooperation found that expectations had the same correlation with cooperation, regardless of when expectations were measured (Balliet & Van Lange, 2013).
Altogether, this evidence does not support an alternative model that cooperation mediates the relation between SVO and expectations. Instead, these prior research findings provide strong reasons to believe that expected cooperation mediates the relationship between
SVO and cooperation in social dilemmas. A relatively more prosocial SVO leads individuals
33 SVO, EXPECTATIONS, AND COOPERATION to expect more cooperation from others, which subsequently makes them more likely to cooperate themselves. Even though both psychologists and economists have prioritized both
SVO (i.e., social preferences; Murphy et al., 2010) and expectations about others’ behavior in predicting behavior in interdependent situations (e.g., Fischbacher & Gächter, 2010; Kuhlman
& Wimberley, 1976), very few studies (e.g., Sheldon, 1999) have directly tested the proposed mediation model (Bogaert et al., 2008). Hence, existing evidence is inconclusive about how strongly SVO corresponds to beliefs about other’s cooperation, and about the role that expectations play in understanding how SVO relates to cooperative behavior. Here, we aim to meta-analytically test this mediation model and to provide an estimate of the magnitude of the indirect effect.
Do proselfs cooperate when they expect their partner to cooperate? Beyond testing the proposed mediation model, we were also interested in evaluating the possibility that the mediation model applies to prosocials, but not to proselfs. Prosocials are predicted to increase their cooperation when they expect their partner to cooperate (Boone, Declerck, &
Suetens, 2008). However, proselfs may prefer to exploit a partner who is expected to cooperate and would also most certainly defect with an uncooperative partner. This reasoning suggests a positive relation between partner expected cooperation and own cooperation among prosocials, but a null relation among proselfs (especially in a one-shot interaction).
Supporting this hypothesis, Boone and colleagues (2010) found that expectations increase cooperative behavior among prosocials, but not among proselfs.
Overview of the Meta-Analysis
In summary, we aim to achieve four goals with this meta-analysis. First, we estimate the magnitude of difference between each category of SVO in their expectations of others cooperation: (1) prosocials versus individualists, (2) prosocials versus competitors; (3) individualists versus competitors; and (4) prosocials versus proselfs. Second, we test several study characteristics as possible moderators of the relation between SVO and expected partner cooperation, such as the type of participant payment, the number of iterations or the group
34 CHAPTER 2 size in a social dilemma. Third, we utilize recent developments in meta-analysis to estimate the magnitude of the indirect effect of expectations in explaining the link between SVO and cooperation. This approach will illuminate the degree of importance of expectations as a psychological process explaining how individual differences in SVO relate to cooperation.
Fourth, we investigate if cooperation is conditional upon expectations for prosocials, but not for proselfs. To do so, we test the relation between expectations and cooperation separately for prosocials and proselfs.
Method
Literature Search and Inclusion Criteria
We systematically searched several scientific databases (Academic Search Premier,
Business Source Premier, EconLit, PsycInfo, PsycARTICLES, SocINDEX) for relevant
English-written articles with the following search terms in the entire text of the article:
(“social value orientation” OR “social motive”) AND (“expectation of cooperation” OR
“expectations of cooperation” OR “expected cooperation”). This search returned 795 articles after duplicates were removed and we inspected all abstracts. If SVO was mentioned in the abstract, then we searched the entire article for the inclusion of SVO, expectations of other’s cooperation, and cooperation in a social dilemma. This way, we included 8 articles with 10 studies. In addition, we searched GoogleScholar and found six additional articles with six effect sizes. When an article was published within the last 10 years, but did not include all necessary statistical information to calculate effect sizes, we contacted the authors and requested additional information. This way, we received data for one additional article with two studies. Lastly, we contacted authors who had published on the topic of interest in the past and received two additional published articles with four studies and four unpublished articles with eleven studies. We also searched the reference lists of all articles deemed relevant in this search for other relevant articles. Finally, we searched all articles included in prior meta-analyses on SVO and cooperation (Balliet et al., 2009) and expectations and cooperation (Balliet & Van Lange, 2013). Overall, we included 21 articles with 33 studies for
35 SVO, EXPECTATIONS, AND COOPERATION the comparison between prosocials and proselfs in expected partner cooperation. The earliest included article was from 1976 and our search was conducted through October 2015.
There were several criteria for inclusion. First, studies had to measure participants’
SVO (e.g. with the TDM, Ring Measure, or Slider Measure). Second, studies had to include a measure of participants’ expectations of other’s cooperation in a social dilemma (e.g., prisoner’s dilemma, public goods dilemma, and resource dilemma).4 Lastly, studies had to involve adult participants (age 18 and above). We excluded studies that classified participants as prosocials or proselfs based on a goal choice in a social dilemma task (e.g., Bixenstine,
Lowenfeld, & Englehart, 1981; Kelley & Stahelski, 1970; Miller & Holmes, 1975). This is a rare measure of social motives, which shares extensive overlap with decisions in social dilemmas and which has not been validated against existing measures of SVO. We also excluded studies using economic games that are not social dilemmas (e.g., ultimatum or dictator games).
Coding of Effect Sizes
Two individuals coded all effect sizes and study characteristics: the first author and a trained research assistant. There was high agreement between coders (96%). All disagreements were resolved through discussion. Each study contained at least one coded effect size, and when possible we coded several different effect sizes from each study
(described below). We used the standardized mean difference as the measure of effect size
(Cohen’s d). Cohen’s d is calculated by dividing the difference between two means by the pooled standard deviation and correcting for sample size (Hedges & Olkin, 1985). We calculated the d value by using the mean and standard deviation of expectations of cooperation for different types of SVOs. When the descriptive statistics were unavailable, we calculated d by using either the t statistic, the F statistic, the Chi-Square value, the proportion of participants expecting cooperation, or the correlation coefficient (r) between SVO and expectations of cooperation. When a manipulated variable was included in a study, we coded the main effect of SVO on expectations of cooperation across conditions. A positive d value
36 CHAPTER 2 indicates that the relatively more prosocial comparison group expects more cooperation than the more proself group (i.e., prosocials > proselfs; prosocials > individualists; prosocials > competitors; individualists > competitors).
We coded four comparisons on the relation between SVO and expectations of cooperation: (1) prosocials versus individualists (k = 20, n = 2,686), (2) prosocials versus competitors (k = 13, n = 1,362), (3) individualists versus competitors (k = 13, n = 726), and
(4) prosocials versus proselfs (k = 33, n = 4,793). We use the first three comparisons to gain a comprehensive understanding of the relationship between SVO and expectations. We use the fourth comparison to test for potential moderators of the relation between SVO and expectations of cooperation and to test the mediation model. Table 2 shows the included studies and their corresponding coded effect sizes and study characteristics.
Coding of Study Characteristics
We coded several study characteristics that vary across the studies included in the meta-analysis for the comparison between prosocials and proselfs. Below we describe each study characteristic we coded and the number of studies with coded effect sizes at each level of the coded variable. Table 2 reports the coding for each study.
SVO. SVO was measured by using the TDM (k = 16; Van Lange et al., 1997), the Ring
Measure (k = 8; Liebrand & McClintock, 1988), the Slider Measure (k = 6; Murphy et al.,
2010), or with decomposed games (k = 3; Messick & McClintock, 1968). Whenever the
Slider Measure was used, we coded the results based on the continuous measurement of SVO
(i.e., we converted the correlation coefficient r to Cohen’s d). A few older studies asked participants to indicate their SVO by choosing between a cooperative or a competitive orientation (k = 15; e.g., Bixenstine et al., 1981; Miller & Holmes, 1975). These studies were excluded from the main analysis because the decisions of participants to cooperate or to compete share extensive overlap with the decisions in the social dilemma, but we also report the results including these studies to provide a comprehensive overview of the literature.
37 SVO, EXPECTATIONS, AND COOPERATION
Table 2 Studies included in the Meta-Analysis Total N OS/IT Study (Prosocial CO DL K P/UP/L O/TY SVO GS PS d LL/UL (#) N) Prosocials versus Proselfs Balliet et al. (2011) 85 (49) SG PD .200 P O IT (2) TDM 2 PUB 0.704 0.245/1.163 Study 2 - Study 3 47 (28) SG PGD .250 P O IT (2) TDM 2 PUB 0.127 0.456/0.710 Balliet et al. (2016) 680 (508) US PD .333 UP O OS TDM 2 PUB 0.264 0.090/0.437 - Balliet (2012) 404 (242) US PD .333 L O OS Slider 2 UPUB 0.066 0.130/0.262 - Study 2 111 (81) NL PD .333 L O OS Slider 2 UPUB 0.199 0.180/0.578 Study 3 341 (170) US PD .333 L O OS Slider 2 UPUB 0.802 0.572/1.031 IT Boone et al. (2008) 73 (42) DK PGD .922 P O TDM 2 PUB 0.602 0.128/1.077 (15) De Bruin & van 144 (77) NL PGD .333 UP O OS TDM 2 PUB 0.429 0.098/0.760 Lange (1999) De Cremer et al. 88 (46) NL PGD .357 P O IT (-) TDM 5 PUB 0.466 0.042/0.890 (2008) Eek & Gärling 54 (34) SE PD .286 UP O OS TDM 2 PUB 0.961 0.249/1.673 (2006) Kiyonari (2011) 130 (62) JP PD .333 P O OS TDM 2 UPUB 0.492 0.143/0.842 Study 2 149 (75) JP PD .333 P O OS TDM 2 UPUB 0.438 0.113/0.763 Study 3 54 (26) JP PD .333 P O OS TDM 2 UPUB 0.939 0.376/1.501 Kiyonari & - 87 (64) CA PGD .444 P O OS TDM 4 PUB 0.273 Barclay (2008) 0.205/0.752 Study 2 73 (54) CA PGD .444 P O OS TDM 4 PUB 0.634 0.101/1.167 - Study 3 108 (78) CA PGD .444 P O OS TDM 4 PUB 0.189 0.233/0.610 Kiyonari et al. 119 (86) BE RD .444 P O OS TDM 4 UPUB 0.588 0.179/0.996 (2008) - Study 2 113 (85) BE PGD .444 P O OS TDM 4 UPUB 0.257 0.171/0.685 Kramer et al. IT - 53 (26) US RD --- P O DG 6 PUB 0.446 (1986) (12) 0.100/0.991 Kuhlman & IT 128 (59) US PD .200 P TY DG 2 PUB 0.423 0.030/0.816 Wimberley (1976) (30) Liebrand et al. 126 (58) NL PD --- P O IT (8) Ring 8 PUB 0.411 0.057/0.765 (1986) Smeesters et al. 186 (95) BE PGD .400 UP O OS Ring 2 PUB 0.325 0.035/0.614 (2003) Study 2 Study 3 128 (62) BE PGD .370 UP O OS Ring 2 PUB 0.349 0.000/0.699 Study 4 155 (81) BE PGD .370 UP O OS Ring 2 PUB 0.376 0.058/0.694 Smeesters et al. - 140 (70) NL PGD .400 UP O OS Ring 2 UPUB 0.223 (2003) 0.109/0.555 Van Lange (1992) 123 (52) NL PD .333 UP O IT (4) DG 2 PUB 0.738 0.293/1.183 Van Lange (1999) 164 (93) NL PGD .333 P O OS Ring 2 PUB 0.593 0.278/0.909 Van Lange & - 78 (45) US PGD .333 P O OS Ring 2 PUB 0.136 Liebrand (1989) 0.314/0.585 Van Lange & - 59 (38) NL PGD .333 P O OS Ring 2 PUB 0.469 Liebrand (1991b) 0.071/1.008 Wu et al. (2013) 119 (97) CN PGD .333 P TY OS Slider 2 UPUB 0.374 0.004/0.745 Study 2 195 (173) CN PD .500 UP TY IT (4) Slider 2 UPUB 0.381 0.093/0.669 - Study 3 186 (151) CN PD .500 UP TY IT (4) Slider 2 UPUB 0.289 0.004/0.582 Yamagishi et al. - 93 (70) JP PD .333 P O OS TDM 2 PUB 0.410 (2013) 0.061/0.881
Prosocials versus Proselfs (with Goal Choice) Bixenstine et al. IT 64 (32) US PD .500 P TY Choice 2 PUB 2.301 0.716/3.887 (1981) (40) IT Study 2 96 (48) US PD .500 P TY Choice 2 PUB 1.867 1.026/2.709 (20) Centers & Kelley 289 (225) US PD --- UP ------Choice --- UPUB 1.522 1.100/1.943 (1969) Study 2 238 (181) US PD --- UP ------Choice --- UPUB 1.852 1.320/2.385 Dorris (1969) 40 (16) ------TY --- Choice --- UPUB 1.077 0.091/2.062
38 CHAPTER 2
Kanouse & Wiest 187 (101) US PD .400 UP O OS Choice 2 PUB 1.863 1.427/2.300 (1967) Kelley et al. (1970) 550 (203) US DM --- UP/P TY IT (-) Choice 2 PUB 1.203 0.928/1.478 Kelley & Stahelski IT 219 (129) US PD 1.00 P TY Choice 2 PUB 0.988 0.643/1.333 (1970) (10) Kelley & Stahelski IT - 101 (52) US PD 1.00 --- TY Choice 2 PUB 0.389 (1970b) (30) 0.085/0.863 Loomis (1959) 198 (111) US PD .500 UP O IT (5) Choice 2 PUB 1.452 1.066/1.839 Messé & Sivacek 172 (73) US PD .467 P O OS Choice 2 PUB 1.156 0.774/1.539 (1979) Miller & Holmes IT 36 (28) CA PD .500 UP TY Choice 2 PUB 1.435 0.095/2.775 (1975) (30) IT - Study 2 34 (26) CA PD .500 UP TY Choice 2 PUB 0.757 (30) 0.175/1.689 Misra & Kalro 249 (70) IN PD .900 UP TY OS Choice 2 PUB 1.168 0.820/1.516 (1979) Schlenker & IT 158 (83) US PD .800 --- TY Choice 2 PUB 0.463 0.048/0.878 Goldman (1978) (30) Prosocials versus Individualists Balliet et al. (2011) 75 (26) SG PD .200 P O IT (2) TDM 2 PUB 0.555 0.010/1.101 Study 2 - Study 3 45 (28) SG PGD .250 P O IT (2) TDM 2 PUB 0.280 0.326/0.885 Balliet et al. (2016) 666 (508) US PD .333 P O OS TDM 2 PUB 0.278 0.099/0.457 IT Boone et al. (2008) 71 (42) DK PGD .922 P O TDM 2 PUB 0.695 0.208/1.182 (15) De Cremer et al. 88 (46) NL PGD .357 P O IT (-) TDM 5 PUB 0.466 0.042/0.890 (2008) Kiyonari (2011) 123 (62) JP PD .333 P O OS TDM 2 UPUB 0.616 0.254/0.977 Study 2 144 (75) JP PD .333 P O OS TDM 2 UPUB 0.431 0.101/0.762 Study 3 51 (26) JP PD .333 P O OS TDM 2 UPUB 0.877 0.302/1.451 Kiyonari & - 86 (64) CA PGD .444 P O OS TDM 4 PUB 0.241 Barclay (2008) 0.245/0.727 Study 2 72 (54) CA PGD .444 P O OS TDM 4 PUB 0.562 0.021/1.103 - Study 3 106 (78) CA PGD .444 P O OS TDM 4 PUB 0.108 0.324/0.540 Kiyonari et al. 117 (86) BE RD .444 P O OS TDM 4 UPUB 0.583 0.166/1.001 (2008) - Study 2 112 (85) BE PGD .444 P O OS TDM 4 UPUB 0.231 0.203/0.665 Kuhlman & IT - 98 (59) US PD .200 P TY DG 2 PUB 0.327 Wimberley (1976) (30) 0.127/0.780 Van Lange (1992) 85 (52) NL PD .333 UP O IT (4) DG 2 PUB 0.762 0.305/1.219 Van Lange (1999) 153 (93) NL PGD .333 P O OS Ring 2 PUB 0.576 0.245/0.907 Wu et al. (2013) 119 (97) CN PGD .333 P TY OS Slider 2 UPUB 0.374 0.004/0.745 Study 2 195 (173) CN PD .500 UP TY IT (4) Slider 2 UPUB 0.381 0.093/0.669 - Study 3 186 (151) CN PD .500 UP TY IT (4) Slider 2 UPUB 0.289 0.004/0.582 Yamagishi et al. - 93 (74) JP PD .333 P O OS TDM 2 PUB 0.191 (2013) 0.314/0.696 Prosocials versus Competitors Balliet et al. (2011) 56 (7) SG PD .200 P O IT (2) TDM 2 PUB 1.694 0.085/3.302 - - Study 3 30 (28) SG PGD .250 P O IT (2) TDM 2 PUB 0.373 1.811/1.065 - Balliet et al. (2016) 522 (508) US PD .333 UP O OS TDM 2 PUB 0.107 0.424/0.638 IT - - Boone et al. (2008) 44 (42) DK PGD .922 P O TDM 2 PUB (15) 0.731 2.158/0.696 - - Kiyonari (2011) 69 (62) JP PD .333 P O OS TDM 2 UPUB 0.325 1.108/0.459 - Study 2 80 (75) JP PD .333 P O OS TDM 2 UPUB 0.452 0.456/1.359 - Study 3 29 (26) JP PD .333 P O OS TDM 2 UPUB 1.448 3.169/6.065 Kiyonari & - Barclay (2008) 80 (78) CA PGD .444 P O OS TDM 4 PUB 1.377 0.042/2.797 Study 3 Kiyonari et al. - 88 (86) BE RD .444 P O OS TDM 4 UPUB 0.610 (2008) 0.794/2.015 Kuhlman & IT 89 (59) US PD .200 P TY DG 2 PUB 0.556 0.039/1.073 Wimberley (1976) (30)
39 SVO, EXPECTATIONS, AND COOPERATION
Van Lange (1992) 90 (52) NL PD .333 UP O IT (4) DG 2 PUB 0.717 0.281/1.153 Van Lange (1999) 104 (93) NL PGD .333 P O OS Ring 2 PUB 0.759 0.126/1.393 Yamagishi et al. 81 (74) JP PD .333 P O OS TDM 2 PUB 0.944 0.155/1.732 (2013) Individualists versus Competitors - Balliet et al. (2011) 33 (7) SG PD .200 P O IT (2) TDM 2 PUB 1.156 0.481/2.793 - - Study 3 19 (17) SG PGD .250 P O IT (2) TDM 2 PUB 0.679 2.160/0.802 - - Balliet et al. (2016) 172 (158) US PD .333 UP O OS TDM 2 PUB 0.165 0.712/0.381 IT - - Boone et al. (2008) 31 (29) DK PGD .922 P O TDM 2 PUB (15) 1.164 2.625/0.297 - -2.079/- Kiyonari (2011) 68 (61) JP PD .333 P O OS TDM 2 UPUB 1.268 0.457 - Study 2 75 (69) JP PD .333 P O OS TDM 2 UPUB 0.050 0.857/0.958 - Study 3 28 (25) JP PD .333 P O OS TDM 2 UPUB 0.681 2.495/3.856 Kiyonari & - Barclay (2008) 30 (28) CA PGD .444 P O OS TDM 4 PUB 1.174 0.291/2.639 Study 3 Kiyonari et al. - 33 (31) BE RD .444 P O OS TDM 4 UPUB 0.025 (2008) 1.405/1.455 Kuhlman & IT - 69 (39) US PD .200 P TY DG 2 PUB 0.230 Wimberley (1976) (30) 0.331/0.790 - - Van Lange (1992) 71 (33) NL PD .333 UP O IT (4) DG 2 PUB 0.045 0.518/0.428 - Van Lange (1999) 71 (60) NL PGD .333 P O OS Ring 2 PUB 0.133 0.511/0.776 Yamagishi et al. - 26 (19) JP PD .333 P O OS TDM 2 PUB 0.654 (2013) 0.231/1.539 Note. Total N = Number of participants in study; Prosocial N = Number of prosocial participants in study; CO = country; SG = Singapore; US = United States; NL = the Netherlands; DK = Denmark; SE = Sweden; JP = Japan; CA = Canada; BE = Belgium; CN = China; DL = Social dilemma in which expectations were assessed; PD = Prisoner’s Dilemma; PGD = Public Goods Dilemma; RD = Resource Dilemma; K = K Index; P = paid; UP = unpaid; L = Lottery; T = Target of expectations; O = other; TY = typical; OS = one-shot; IT(##) = iterated (number of iterations); SVO = Measure of SVO; TDM = Triple Dominance Measure; DG = decomposed game measure; Slider = SVO slider measure; Ring = Ring measure of SVO; GS = Group size; PS = Publication status; PUB = Published; UPUB = Unpublished; d = Cohen’s d; LL/UL = 95% confidence interval with lower and upper limit.
Type of dilemma. We coded the type of social dilemma in the study, including the prisoner’s dilemma (PD; k = 15), public goods dilemma (PGD; k = 16), and resource dilemma
(RD; k = 2). In the PD and PGD, individuals decide how much to contribute to a common shared pool, which subsequently accumulates interest (e.g., is doubled) and is then evenly distributed among all participants. Thus, individuals face the temptation to benefit from others’ contributions while not contributing themselves. In the RD, individuals decide how much to take from a common shared resource, which is depleted if a certain threshold is reached. In this situation, participants are tempted to take as much as possible, while taking too much can deplete the resource. We reverse coded effect sizes with the RD, so that higher scores indicate greater cooperation.
40 CHAPTER 2
The social dilemmas vary on how much conflict they contain between individual and collective interests. Therefore, we coded the index of cooperation (K index), which can range from 0 to 1 and is calculated by (R – P) / (T – S). A lower value indicates a higher degree of conflict between individual and collective interests. We coded 31 studies, for which the K index ranged between 0.20 and 0.92 (M = 0.38, SD = 0.13).
Target of expectations. Participants were asked how much cooperation they expected from the other individual(s) in the social dilemma. Most studies assessed expectations about the specific other person in the social dilemma (k = 29), but a few other studies measured expectations about a typical other person (e.g., the typical student; k = 4).
Additional codings. We coded whether participants were paid for the outcomes in the social dilemma (k = 20), received lottery tickets (k = 3), or were asked to imagine that they were playing for something valuable (i.e., hypothetical outcomes; k = 10). Participants either interacted in a one-shot (k = 23) or in an iterated social dilemma (k = 10). We also coded the number of iterations as a continuous variable ranging from 1 to 30 (Median = 1; Mode = 1; M
= 3.06, SD = 5.04). We coded whether participants interacted in a dyad (k = 25) or in a group of three or more individuals (k = 8). Group size was also coded as a continuous variable, ranging from 2 to 8 (Median = 2, M = 2.70, SD = 1.42). We included both published (k = 21) and unpublished studies (k = 12). Most studies were conducted in the Netherlands (k = 9) and in the USA (k = 6). Other countries represented in the sample include Belgium, Canada,
China, Denmark, Japan, Singapore, and Sweden. Studies were published (or conducted, for unpublished studies) between 1976 and 2016 (Median = 2008).
41 SVO, EXPECTATIONS, AND COOPERATION
Overview of Analysis
Overall estimated effect sizes. We use Cohen’s d as a measure of effect size and conduct the meta-analysis in Comprehensive Meta-Analysis (CMA) software using inverse variance weights (Borenstein, Hedges, Higgins, & Rothstein, 2009). The overall analyses are conducted using a random effects model because we did not assume to have sampled all studies out of the population of studies and because we assumed that the effect size differs between studies due to differences in study characteristics. In addition to the mean weighted overall effect size, we report the 95% confidence interval and the 90% prediction interval
(Hedges & Olkin, 1985). Next, we examine the variation in the overall effect size using indicators of heterogeneity of variance (T, T2, and I2). T2 is an index of between-study variance (DerSimonian & Laird, 1986). The I2 index measures variability in effect sizes due to real (as opposed to chance) differences between studies (25% = low, 50% = moderate, 75%
= high; Higgins, Thompson, Deeks, & Altman, 2003).
We then use multiple indices to test for the possibility of publication bias in our sample.
First, we report the distribution of studies in a funnel plot (in which all studies are plotted according to their sample size and standard error). We use Duval and Tweedie’s (2000) trim- and-fill method to assess the symmetry of the effect size distribution in the funnel plot. This method removes small studies at the extremes, while the effect size is recalculated at each iteration until symmetry is achieved. Publication bias is present if the interpretation of the newly estimated effect size differs from the interpretation of the observed effect size.
However, readers should interpret results from the trim-and-fill method with caution: This method might underestimate the effect size because it corrects for publication bias that does not exist (Terrin, Schmid, Lau, & Olkin, 2003) or overestimate the effect size because it does not adequately correct for publication bias that does exist (Carter, Hilgard, Schönbrodt, &
Gervais, 2017). Second, we report Begg and Mazumdar’s rank correlation (Begg &
Mazumdar, 1994), which provides a correlation between the ranks of effect sizes and the ranks of their variances, and Egger’s regression intercept (Egger, Davey Smith, Schneider, &
42 CHAPTER 2
Minder, 1997), which regresses the standard normal deviate on the study’s precision.
Statistically significant results indicate possible publication bias in the data. These analyses were conducted with Comprehensive Meta-Analysis software. Third, we examine if published studies show larger effect sizes than unpublished studies, which would indicate publication bias. In addition, it is possible that the selective reporting of statistically significant results within primary studies influenced our meta-analytic results. While this possibility cannot be ruled out, we believe that it is not very likely that it influenced the results of the current meta- analysis because the relation between SVO and expectations was often not the main focus of published studies and because we included several unpublished studies.
Moderation analyses. We test for possible moderators of the relation between SVO and expectations of other’s cooperation. For these moderation analyses, we employ Robust
Variance Estimation (RVE), a random-effects meta-regression that can account for dependent effect sizes (Hedges, Tipton, & Johnson, 2010), even when only a small number of studies are included (Tipton, 2015). This method allows us to conduct moderator analyses simultaneously on all included effect sizes as opposed to conducting them on only one comparison (i.e., prosocials versus proselfs), and therefore increases the power of the moderator analyses. Because the effect sizes are nested within studies, we use correlated effects RVE with random-effect weights, and report robust t tests (results are only trustworthy if df > 4). We conduct these analyses using the robumeta package in R and set rho at the recommended .80 (Tanner-Smith & Tipton, 2014). Whenever a moderator was categorical with three levels (e.g., SVO measure: TDM, Ring, Slider), we created dummy variables and compared each moderator level against all others (e.g., 1 = Slider, 0 = Other).
Meta-analytic mediation model. We test the hypothesis that expectations of other’s cooperation mediate the relation between SVO and own cooperation in social dilemmas. To conduct the meta-analytic mediation test, we coded two additional effect sizes: (1) SVO predicting own cooperation; and (2) expectations of other’s cooperation predicting own cooperation. We used recent meta-analyses (Balliet et al., 2009; Balliet & Van Lange, 2013)
43 SVO, EXPECTATIONS, AND COOPERATION and examined all studies measuring the relationship between SVO and expectations to obtain these effect sizes. Studies had to report at least two of the three effect sizes of interest to be included in the meta-analysis.5 In a few cases, the sample sizes differed between those three coded effect sizes per study, in which we coded the average sample size across the three effect sizes. Table 3 reports the studies and their corresponding coded effect sizes for all studies included to test the mediation model.
To test the mediation model, we used the correlation coefficient (r) as the measure of effect size. When the correlation was not reported in the article, we used the same statistics mentioned above to calculate the correlation coefficient (r). For the correlation between SVO and expectations, a positive correlation indicates that the relatively more prosocial participants expect more cooperation from others than relatively more proself participants (k =
32, n = 4,689). The same holds for the correlation between SVO and cooperation: A positive correlation indicates that the relatively more prosocial participants cooperate more than the relatively more proself participants (k = 39, n = 5,521). A positive correlation between expectations and cooperation indicates that higher levels of expected cooperation are associated with higher levels of cooperation (k = 34, n = 4,932).
We adopted a two-stage random-effects meta-analytic structural equation modeling
(MASEM) approach to examine the hypothesized mediation effect (Cheung, 2015). This approach combines meta-analysis with structural equation modeling. In the first stage, the correlations between all variables (i.e., SVO, expectations, cooperation) from all primary studies are synthesized into one pooled correlation matrix.
44 CHAPTER 2
Table 3 Studies included in the Meta-Analytic Test of Mediation SVO - EXP SVO - COOP EXP - COOP Study N r N r N r Coded N Balliet et al. (2011) Study 2 85 .332 84 .370 93 .402 87 Study 3 47 .062 49 .220 59 .443 51 Balliet et al. (2016) 680 .114 682 .310 726 .707 696 Balliet (2012) 404 .033 404 .210 404 .517 404 Study 2 111 .099 111 .160 111 .690 111 Study 3 341 .372 341 .160 341 .751 341 Boone et al. (2008) 73 .285 73 .251 73 .645 73 De Bruin & van Lange (1999) 144 .209 144 .324 ------144 De Cremer et al. (2008) 88 .227 88 .205 ------88 De Dreu & McCusker (1997) ------74 .520 83 .420 78 Eek & Gärling (2006) 54 .421 54 .460 54 .853 54 Kiyonari (2011) 130 .239 131 .391 130 .811 130 Study 2 149 .214 150 .377 149 .539 149 Study 3 54 .425 54 .477 54 .589 54 Kiyonari & Barclay (2008) 87 .120 87 .182 87 .539 87 Study 2 73 .268 73 .378 73 .487 73 Study 3 108 .084 108 .220 108 .503 108 Kiyonari et al. (2008) 119 .254 119 .285 119 .419 119 Study 2 113 .110 113 .387 113 .294 113 Kramer et al. (1986) 53 .217 53 .370 ------53 Liebrand et al. (1986) 126 .201 126 .310 48 .810 100 Smeesters et al. (2003) ------102 .330 203 .590 152 Study 2 186 .160 192 .400 193 .590 190 Study 3 128 .172 132 .420 140 .850 133 Study 4 155 .184 167 .490 167 .590 163 Smeesters et al. (2003) 140 .111 140 .323 ------140 Stouten et al. (2005) ------79 .290 108 .410 93 Van Lange (1992) 123 .342 123 .340 144 .800 130 Van Lange (1999) 164 .282 164 .320 ------164 Van Lange & Kuhlman (1994) ------334 .270 334 .670 334 Van Lange & Liebrand (1989) 78 .067 78 .340 87 .610 81 Van Lange & Liebrand (1991a) ------59 .390 59 .750 59 Study 2 ------56 .340 56 .530 56 Van Lange & Liebrand (1991b) 59 .219 55 .360 55 .380 56 Study 2 ------60 .420 60 .570 60 Wu et al. (2013) 119 .184 119 .299 119 .724 119 Study 2 195 .187 198 .238 195 .680 196 Study 3 186 .143 197 .176 186 .693 189 Yamagishi et al. (2013) 93 .172 93 .201 93 .812 93 Note. N = Number of participants in study; Coded N = average number of participants across all three effect sizes coded for the MASEM.
45 SVO, EXPECTATIONS, AND COOPERATION
In the second stage, this meta-analytic correlation matrix is treated as an observed correlation matrix and subjected to a structural equation model to test the hypothesized mediation effect. A mediation effect of expected cooperation on the relation between SVO and cooperation would be present if the indirect effect is significant, while the direct effect decreases in magnitude or becomes nonsignificant. The MASEM analyses were conducted using default values in R with the metaSEM package (Cheung, 2014).
Expectations and cooperation: Prosocials versus Proselfs. To examine if expectations and cooperation are positively related among prosocials, but not among proselfs, whenever possible we coded the correlation coefficient (r) between expectations and cooperation and the sample size N, separately for prosocials and proselfs (see Table 4). Then, we applied the same meta-analytic techniques outlined above that were used to examine the relation between SVO and expectations.
The Open Science Framework webpage for this article is: http://osf.io/2dc4p. This webpage contains the dataset and R script for all analyses conducted using R.
Table 4 Studies included in the Meta-Analyses on Expectations and Cooperation Separately for Prosocials and Proselfs Prosocials Proselfs Overall Study N r N r N r Balliet et al. (2011) Study 2 48 .393 35 .252 93 .402 Study 3 30 .638 19 .085 59 .443 Balliet et al. (2016) 508 .701 172 .721 726 .707 Balliet (2012) 249 .511 155 .550 404 .517 Study 2 81 .796 30 .655 111 .690 Study 3 170 770 171 .614 341 .751 Boone et al. (2008) 42 .774 31 .472 73 .645 Wu et al. (2013) 97 .699 22 .779 119 .724 Study 2 173 .693 22 .531 195 .680 Study 3 151 .691 35 .674 186 .693 Note. N = Number of participants in study.
46 CHAPTER 2
Results
SVO and Expectations: Overall Estimated Effect Sizes
We begin by first reporting the estimated average population effect size for each comparison for SVO and expectations of cooperation. For each comparison, we report the overall weighted effect size (with a corresponding confidence interval and prediction interval), estimates of heterogeneity in the effect size distribution, and three estimates of the presence of publication bias (see Table 5).
Prosocials versus individualists. Prosocials expected significantly more cooperation from others than individualists, d = 0.402, 95% CI [0.319, 0.485], 90% prediction interval
[0.330, 0.474], p < .001. There was no variance in the true effect size distribution (T = 0.000,
T2 = 0.000, I2 = 0.00). We used Duval and Tweedie’s (2000) trim-and-fill method to examine publication bias. No effect sizes were imputed above the overall effect size, but four were imputed below the overall effect size, which did not change the overall effect size substantially, d = 0.359, 95% CI [0.270, 0.449]. Begg and Mazumdar’s rank correlation (p =
.284) as well as Egger’s regression intercept (p = .090) were nonsignificant, suggesting that publication bias did not significantly influence these results.
Prosocials versus competitors. Prosocials expected significantly more cooperation from others than competitors (d = 0.481, 95% CI [0.197, 0.764], 90% prediction interval [-
0.057, 1.019], p < .01). There was substantial variation in the true effect size distribution (T =
0.270, T2 = 0.073), and some of this variation could be explained by systematic differences between studies (I2 = 30.52). The trim-and-fill method (Duval & Tweedie, 2000) imputed only two effect sizes below the overall weighted effect size, which did not substantially change the interpretation of the effect size, d = 0.440, 95% CI [0.156, 0.724], p < .01. Begg and Mazumdar’s rank correlation (p = .760) as well as Egger’s regression intercept (p = .989) were nonsignificant, indicating that publication bias did not significantly influence the results of this analysis.
47 SVO, EXPECTATIONS, AND COOPERATION
Individualists versus competitors. Individualists and competitors did not significantly differ in their expectations of cooperation, d = -0.022, 95% CI [-0.349, 0.306],
90% prediction interval [-0.716, 0.672], p = .896. There was variation in the true effect size distribution (T = 0.359, T2 = 0.129), and part of that variation could be explained by between- study differences (I2 = 41.33). Using Duval and Tweedie’s (2000) trim-and-fill method, three studies were imputed below the estimated effect size, but the interpretation of the overall estimated effect size did not change (d = -0.131, 95% CI [-0.465, 0.203]). Begg and
Mazumdar’s rank correlation (p = .669) and Egger’s Regression intercept (p = .775) were nonsignificant, suggesting an absence of publication bias for this comparison. Thus, across each of the three comparisons we did not find evidence that our sample of effect sizes was contaminated by publication bias.
Moderators of the SVO-Expectation Relation
We conducted several univariate moderator analyses to test whether specific study characteristics moderate the relation between SVO and expectations. In all moderation analyses, we focus on the comparison between prosocials and proselfs. Overall, prosocials expected greater cooperation than proselfs (d = 0.405, 95% CI [0.329, 0.481], 90% prediction interval [0.194, 0.616], p < .001).6 There was variation in the true effect size distribution (T =
0.118, T2 = 0.014), which can be explained in part by differences between studies (I2 = 30.62).
Figure 1 displays the funnel plot for this comparison. Using the trim-and-fill method (Duval
& Tweedie, 2000), eleven studies were inserted below the estimated effect size. The re- estimated effect size (d = 0.300, 95% CI [0.213, 0.388]) differed from the original effect size estimate (d = .405), but the confidence intervals still overlap. Begg and Mazumdar’s rank correlation (p = .086) was nonsignificant, whereas Egger’s regression intercept (p = .050) was significant. However, published studies did not show a larger effect size (d = .395, k = 21) than unpublished studies (d = .402, k = 12), Q(1) = 0.005, p = .945. The publication status also did not moderate the relation between SVO and expectations when testing it on the entire
48 CHAPTER 2 sample of studies using RVE moderator analyses (see Table 6). Overall, we find mixed evidence that publication bias could have influenced the results of this analysis.
Table 6 shows the results of the univariate categorical and continuous moderator analyses using RVE for meta-analyses (Hedges et al., 2010; Tipton, 2015). Whenever the degrees of freedom of a moderation analysis were smaller than four, the results should not be trusted and we therefore omitted them from Table 6 (Tipton, 2015). This holds for the following moderators: payment (1 = lottery, 0 = other), the classification of SVO (1 = decomposed games, 0 = other), the continuous codings of group size, and the social dilemma
(1 = resource dilemma, 0 = other). The overall conclusion from these analyses is that none of the coded study characteristics significantly moderated the relation between SVO and expectations of other’s cooperation.
49 SVO, EXPECTATIONS, AND COOPERATION
p .050 .009 .090 .989 .775 ER
value value for
- p p .086 .007 .284 .760 .669 Publication Bias B&M sided -
2 = two I 0.00
30.62 80.36 30.52 41.33 p
2 T 0.014 0.149 0.000 0.073 0.129
Heterogeneity
T 0.118 0.386 0.000 0.270 0.359
90% PI 0.018, 1.306] 0.057, 1.019] 0.716, 0.672] [0.194, 0.616] - [0.330, 0.474] - - [ [ [
0.485]
95% CI 0.349, 0.306] [0.329, 0.481] [0.516, 0.771] [0.319, [0.197, 0.764] - [ value value for Intercept. Egger’s Regression -
p Overall Effect Size
d 0.405 0.644 0.402 0.481 0.022 - sided sided -
N 726 = Cohen’s d; B&M confidence PI = interval; interval; CI prediction = = Cohen’s = two
4793 7414 2686 1362 d p
k 33 48 20 13 13
versus Proselfsversus = number of included effect sizes; included sizes; = of effect number
With Goal Choice With
Type Effect of Size Prosocials Individualists versus Prosocials Competitors versus Prosocials Individualists Competitors versus Table 5 EffectAverage and Overall Publication BiasSizes, Heterogeneity Note. k &Begg ER rank Mazumdar’s correlation;
50 CHAPTER 2
Figure 1 Funnel Plot for the Comparison between Prosocials and Proselfs on Expected Cooperation in Social Dilemmas
Note. The x-axis displays the studies’ effect size (Cohen’s d values). The y-axis shows the studies’ precision (standard error of Cohen’s d). Circles indicate individual studies. The vertical line shows the overall weighted effect size.
51 SVO, EXPECTATIONS, AND COOPERATION
98
2 41.96 42.03 41.96 41.72 41. 42.00 42.03 41.60 42.04 42.02 44.66 41.06 I
regression; regression; -
2 T .032 .033 .033 .032 .032 .033 .033 .031 .033 .033 .036 .031 l dilemma = (1
payment (1 = lottery, 0 payment = (1 lottery, p 998 .968 .955 .840 .603 . .674 .941 .401 .825 .715 .188 .384 squared estimate based on based squared estimate
-
df 4.08 9.36 8.84 4.57 = tau 25.80 19.20 12.10 19.30 12.80 23.40 24.10 19.63 2 T
; 2
t 0.040 0.215 0.534 0.880 0.224 0.891 0.057 0.00 0.431 0.076 0.369 1.550 value ------
= p
ß p 180 , 0. 0.142, .167 0.097, .221 - - 0.160, 0.167 0.166, 0.157 0.112, 0.185 0.181 0.179, 0.119 0.294, 0.275 0.163, 0.203 0.204, 0.142 1.520, 0.400 0.114, 0.283 ------95% for CI
6
SE 0.079 0.077 0.056 0.068 0.08 0.069 0.126 0.070 0.089 0.084 0.364 0.095 = degrees freedom;of = degrees
df 00 ;
ß ß 0.003 0.012 0.037 0.062 0.020 0.085 0.004 0.0 0.030 0.010 0.031 0.562 ------ator Analyses on the SVO and Expectations of Effecton Cooperation ator Analyses Expectations the Sizes and SVO
0.361 0.365 0.353 0.352 0.363 0.372 0.366 0.350 0.353 0.378 0.568 0.314 > 4, we omitted the following moderator analyses from this table: followingmoderator from > this the 4, we table: omitted analyses Intercept df
k 79 79 79 79 79 79 79 79 79 79 77 79
n = number of included effect sizes nested within of = nested meta Intercept intercept studies; the included sizes of effect number = 33 33 33 33 33 33 33 33 33 33 31 33
k
are results only if trustworthy Because
Unpaid, 0 Unpaid, = Other Other PD, 0 =
= number of included = of studies; number Variables and Variables Codings
n 1 = Other Paid, 0 = 1 = 1 = TypicalOther, 0 = 1 = = Yes, No 0 1 = Other 0 TDM, = 1 = 0 Ring, = Other 1 = Slider, = Other 0 1 = = than two more two, 0 1 = 1 = Other PGD, 0 = Continuous 1 = 0 Published, = . = unstandardized regression coefficient; SE = standard error of SE = = regression coefficient; unstandardized standard
Payment Target Expectation of Iterations of Classification SVO Group Size Dilemma K Index Publication Status Unpublished Table 6 ModerResults of the and Categorical Univariate Continuous Note ß rho.80. = socia size, group = games, and other),continuous of codings 0 other), (1the decomposed the = SVO the of = classification resource dilemma, = other) 0
52 CHAPTER 2
Do Expectations Mediate the SVO-Cooperation Relation?
In the first step of testing the mediation model, we estimated an overall pooled correlation matrix using all effect sizes from primary studies that contain at least two of the three correlations of interest (see Table 7). Each effect size distribution contained variation that could be explained by systematic differences between studies (I2 ranging from 39.70% to
89.34%; see Table 7). In addition, we can reject the null hypothesis of homogeneity of variance of the correlation matrix (Q(102) = 538.81, p < .001). These results support our decision to apply a random-effects model. Replicating the results of prior meta-analyses
(Balliet et al, 2009; Balliet & Van Lange, 2013), we found a medium-sized overall correlation between SVO and cooperation (r = .317, p < .001),7 and a large overall correlation between expectations and cooperation (r = .626, p < .001). The correlation between SVO (prosocial vs. proself) and expectations (r = .207, p < .001) also replicates the effect size reported above (d
= 0.405 or r = 0.195). The observed correlations, standard errors, confidence intervals, and estimates of the between-study variance are displayed in Table 7.
Table 7 Overall Average Effect Sizes and Heterogeneity included in the Meta-Analytic Mediation Model Relationship k N r SE 95% CI I2 SVO – EXP 32 4689 .207 .019 [.170, .244] 42.20 SVO – COOP 39 5521 .317 .016 [.286, .349] 39.70 EXP – COOP 34 4932 .626 .025 [.577, .676] 89.34 Note. k = number of included effect sizes; N = number of participants; SE = standard error; CI = confidence interval.
In the second step, we estimated the mediation effect by fitting a structural equation model to the pooled meta-analytic correlation matrix. Because the proposed mediation model is a just identified (saturated) path analysis model, the chi-square statistic for the model is 0 and the goodness-of-fit-indices common to structural equation modelling are not applicable
(Cheung, 2015). Figure 2 displays the path diagram for the mediation model fitted to the pooled meta-analytic correlation matrix. Although the direct effect remained significant (c’ =
0.196, 95% CI [0.160, 0.232]), it decreased in magnitude compared to the meta-analytic
53 SVO, EXPECTATIONS, AND COOPERATION estimate of the effect size (c = 0.317, 95% CI [0.286, 0.349]). The indirect effect of SVO on cooperation via expectations was statistically significant (a*b = 0.121, 95% CI [0.098,
0.146]). These results provide evidence for partial mediation (Baron & Kenny, 1986).
Does the Expectations-Cooperation Relation Differ between Prosocials and Proselfs?
We meta-analyzed the correlation between expectations and cooperation separately for prosocials and proselfs. For prosocials, there is a strong positive correlation between expectations and cooperation (r = .684, k = 10, N = 1549, 95% CI [.617, .741], p < .001).
There was variation in the true effect size distribution (T = 0.155, T2 = 0.024), and parts of this variation could be explained by systematic differences between studies (I2 = 76.99).
Using Duval and Tweedie’s (2000) trim-and-fill method, one study was imputed below the overall weighted effect size, but this did not substantially change the interpretation of the effect size, r =.669, 95% CI [.601, .728]. Begg and Mazumdar’s rank correlation (p = .999) and Egger’s regression intercept (p = .961) were both nonsignificant, indicating an absence of publication bias. For proselfs, there was also a strong positive correlation between expectations and cooperation (r = .581, N = 692, k = 10, 95% CI [.476, .669], p < .001).
Again, there was substantial variation in the true effect size distribution (T = 0.172, T2 =
0.030) and this might be explained by systematic differences between studies (I2 = 63.71).
Duval and Tweedie’s (2000) trim-and-fill method did not impute any effect sizes, and Begg and Mazumdar’s rank correlation (p = .592) and Egger’s regression intercept (p = .280) were nonsignificant as well. The relation between expectations and cooperation did not significantly differ between prosocials and proselfs, Q(1) = 3.314, p = .069.8
54 CHAPTER 2
Figure 2 Path Diagram of the Meta-Analytic Mediation Model of Expectations Mediating the Effect of SVO on Cooperation
Note. a = Effect of SVO on Expectations, b = Effect of Expectations on Cooperation; a*b = Indirect effect, c = Total effect, c’ = Direct effect in the full mediation model; * p < .01
Discussion
People experience a wide variety of interdependent situations with others in their day- to-day lives. In these situations, the decisions and actions of each person can impact their own and other’s outcomes. Expectations of other’s behavior in interdependent situations are essential to enable successful coordination, avoid exploitation, and to achieve mutually beneficial outcomes (Holmes, 2002), and this is especially true in interdependent situations that involve a conflict of interests, such as social dilemmas (Balliet & Van Lange, 2013). Yet, in many social dilemma situations, people do not have any information about their partners.
Previous theory suggests that personality may play a pivotal role in forming expectations of others’ behavior (Holmes, 2002; Rusbult & Van Lange, 2003). By far, most attention has been paid to how SVO relates to expectations of partner cooperation in social dilemmas (e.g.,
Balliet & Van Lange, 2013; Kuhlman & Wimberley, 1976). However, studies have remained inconclusive about the magnitude of the effect of SVO on expectations, and especially if there is a meaningful difference in the amount of expected partner cooperation between
55 SVO, EXPECTATIONS, AND COOPERATION individualists and competitors. Moreover, existing research has not provided a strong test of the claim that expectations play an essential role in mediating the relation between SVO and cooperation or that SVO moderates the relation between expectations and cooperation.
We applied meta-analysis to summarize nearly 50 years of research on the relation between SVO and expectations of partner cooperation in social dilemmas. Furthermore, we utilized meta-analytic structural equation modeling to examine the proposed mediation of expected cooperation on the relationship between SVO and cooperation in social dilemmas.
We found a moderate association between SVO and expected cooperation in social dilemmas.
Prosocials expected significantly more cooperation than individualists (d = 0.402) and competitors (d = 0.481), but there was no significant difference in expected cooperation between individualists and competitors (d = -0.022). The relation between SVO and expectations generalized across variations in the studies, including the type of social dilemma, group size, participant payment, and number of iterations. Furthermore, we replicated the results of previous meta-analyses that both SVO (r = 0.318) and expectations (r = 0.626) are related to cooperative behavior (Balliet et al., 2009; Balliet & Van Lange, 2013).
Complementing these findings, we further demonstrated that expectations partially mediate the relation between SVO and cooperation. We also found that both prosocial and proselfs increase their cooperation when they expect their partner to cooperate. Together, these findings illuminate the important role expectations play in determining and facilitating cooperative behavior in social dilemmas for both prosocials and proselfs.
SVO and Expectations
In social dilemmas, one’s own outcomes are jointly determined by one’s own actions and the actions of one’s partner. In many social dilemma situations, people face a great deal of uncertainty about the consequences of their decisions, largely because there is no information about how others will behave. In the absence of information about how others behave, one’s own preferences can be a cue on which to base expectations of other’s behavior, and this process tends to be automatic, intuitive, and difficult to change with
56 CHAPTER 2 explicit contradictory information (for an overview, see Krueger, 2007). Indeed, we found evidence that individuals with internalized, dispositional prosocial values expect more cooperative behavior from others across different types of social dilemmas and independently of which SVO measure was used. Individuals project their own preferences onto others
(Krueger, 2007), and this can form the basis of beliefs about others’ behavior in interdependent situations.
While the results of the meta-analysis support a social projection process, the results do not allow a comparison of the three theories explaining why and how SVO relates to expectations (i.e., triangle hypothesis, SASB, cone model). This is because these theories make predictions about the expectations people have about the distribution of SVO in the population and not directly about expected cooperation in social dilemmas. However, Aksoy and Weesie (2012) provided convincing evidence in support of the cone model by not only assessing expectations, but also variance in expectations. According to the cone model, social projection, which is assumed to maximize the expected accuracy of one’s own prediction
(Krueger, 2007), is used by prosocials, individualists, and competitors when they project their own preferences onto others to form expectations. Nonetheless, general conceptions and stereotypes about individuals as selfish but not competitive (Miller & Ratner, 1998; Vuolevi
& Van Lange, 2010; Vuolevi & Van Lange, 2012) can lead individualists to expect even less cooperation from others compared to either prosocials or competitors. This also becomes evident as Aksoy and Weesie (2012) found less variability in expectations among individualists as compared to prosocials and competitors.
Previous research was inconclusive about how individualists and competitors would differ in their expectations of others’ behavior. For example, some previous research suggested that individualists form intermediate expectations of cooperation, somewhere between prosocials and competitors (e.g., Van Lange, 1992). Individualists are likely to have a more varied history of interactions with others, because they will cooperate (and so elicit cooperation from others) in a broader range of situations when cooperation is in their self-
57 SVO, EXPECTATIONS, AND COOPERATION interest, such as during possible repeated interactions (Van Lange, Klapwijk, & Van Munster,
2011), when behavior can have reputational consequences (Wu, Balliet, & Van Lange, 2015), and in the presence of possible punishment or rewards (Boone et al., 2010). Competitors tend to defect across a broader range of situations, have difficulties even learning how to maintain cooperation, and so tend to elicit greater non-cooperation from others (McClintock &
Liebrand, 1988; Sattler & Kerr, 1991; Sheldon, 1999). Therefore, if past experiences partly inform expectations of other’s behavior, individualists may expect greater cooperation than competitors. In the present meta-analysis, individualists and competitors did not differ in their expectations of other’s cooperation. One possible explanation is that non-cooperation in social dilemmas is the dominating strategy for both individualists and competitors (Dawes, 1980).
Therefore, in social dilemmas, individualists and competitors do not differ in their expectations of others’ cooperation, because their different goals can be achieved by the same non-cooperative choice. However, when expectations are assessed in decomposed games for which a dominant choice exists for each SVO, expectations differ significantly between individualists and competitors (Kuhlman & Wimberley, 1976). Future research may benefit from further examining how individualists and competitors differ in their expectations of others’ cooperation across various types of interdependent situations (e.g., stag hunt, battle of the sexes, and maximizing differences) and across settings known to affect cooperation (e.g., incentives, communication, and anonymity).
Expectations Mediate the SVO – Cooperation Relation
Previous research has focused on how SVO and expectations of others’ cooperation each independently foster cooperative behavior (e.g., Balliet et al., 2009; Balliet & Van
Lange, 2013). However, it was largely overlooked how these stable cooperative preferences
(i.e., SVO) might lead to increased expected cooperation, which in turn fosters cooperation.
Using an innovative meta-analytic approach, this study is the first to provide robust evidence for partial mediation: Individuals with a relatively more prosocial SVO are more likely to cooperate than proself individuals, in part because they expect more cooperation from others.
58 CHAPTER 2
Thus, SVO exerts a direct effect on cooperative behavior and an indirect effect on cooperation via influencing expectations about partner cooperation.
Altogether, these results provide support for Bogaert and colleagues' (2008) assertion that expectations mediate the relationship between SVO and cooperation. As such, cooperative behavior is more likely to emerge and to be maintained if individuals with prosocial values expect others to cooperate. However, it needs to be noted that - due to the correlational nature of the data - cooperative behavior could also lead to higher levels of expected cooperation (Thielmann & Hilbig, 2014). Expectations and cooperative behavior are mutually reinforcing processes, but a wide variety of experimental studies on social dilemmas suggest that expectations can cause cooperation (Balliet & Van Lange, 2013; Boone et al.,
2010; Iedema & Poppe, 1994a, 1999; Kelley & Stahelski, 1970; Kuhlman et al., 1986;
Kuhlman & Marshello, 1975; Kuhlman & Wimberley, 1976; Van Lange, 1992).
While prosocials aim to achieve collective welfare by cooperating in social dilemmas, the results indicate that relatively more prosocial individuals do not cooperate at all costs.
Instead, the likelihood of cooperation among prosocials increases if they expect others to cooperate as well. This is in line with findings from Kuhlman and Marshello (1975), who found that prosocials show high levels of cooperation in an iterated PD unless their partner consistently defects. For proselfs, behavior of their partner did not matter as much:
Competitors consistently defect independently of their partner’s actions, whereas individualists would only cooperate with a partner pursuing a tit-for-tat strategy. In support of this, Boone, Declerck, and Kiyonari (2010) showed that expecting cooperation fosters cooperation for prosocials, whereas expectations do not influence proselfs’ cooperative behavior.
Based on this previous research, prosocials, but not proselfs, would be predicted to condition their cooperation on their partner’s expected cooperation. Indeed, proselfs could maximize their own short-term outcomes by exploiting a partner they expect will cooperate.
However, we found that both prosocials and proselfs equally, and strongly, condition their
59 SVO, EXPECTATIONS, AND COOPERATION cooperation on their partner’s expected cooperation.9 Yet, proselfs expect much less cooperation from others than prosocials. These findings suggest that proselfs may be encouraged to cooperate by reinforcing expectations of partner cooperation. In fact, even proselfs may maximize their own long-term outcomes by forming mutually beneficial cooperative relationships. Taken together, these findings indicate that expectations are equally important for prosocials and proselfs.
Broader Implications
Although this meta-analysis examined dispositional preferences for cooperation and expectations of other’s cooperation in social dilemmas, the results contain insight about a broad range of scientific topics and societal challenges. Below, we discuss implications for future research in social and personality psychology and for the promotion of cooperative behavior in many societal social dilemmas, such as public good and resource dilemmas.
Personality, SVO, and social behavior. Personality can determine the construal of situations and the goals individuals pursue in social interactions (Sherman, Nave, & Funder,
2013), partly by affecting the expectations these individuals hold. Thus, the beliefs individuals have about others’ behavior in such interdependent situations can at least partially explain the link between personality and behavior. The current meta-analysis is aligned with this perspective on the importance of personality in the construal of situations (Sherman et al.,
2013), and how people approach and perceive others (e.g., Felfe & Schyns, 2010; Fong &
Markus, 1982).
SVO is a relatively narrow personality trait. However, it shares significant overlap with the broader personality dimension of Honesty-Humility in the HEXACO (and with Big
Five Agreeableness) (Hilbig et al., 2014). Research is needed to further consolidate SVO in broader models of personality and to establish if SVO is a facet of specific personality traits, such as Honesty-Humility and Agreeableness. For example, individuals high on Honesty-
Humility weigh their own and others’ outcomes equally strong, indicating a prosocial preference for fairness in outcomes. Demonstrating the generalizability of our findings to a
60 CHAPTER 2 broader personality construct, Pfattheicher and Böhm (2017) found that the relation between
Honesty-Humility and cooperation in a trust game was mediated by social expectations about the trustworthiness of others. To further examine if our findings generalize to broader personality constructs, future research could examine if individuals scoring high on Honesty-
Humility expect others to score similarly high on Honesty-Humility, especially with limited information about the other (i.e., social projection), which would subsequently lead to more cooperative behavior with the other. It might be that such a process is fully mediated by SVO.
For example, people who are high on Honesty-Humility tend to think situations contain less conflict of interests, but this is completely mediated by SVO (Gerpott, Balliet, Columbus,
Molho, & De Vries, 2017). Furthermore, those perceptions of conflict partially mediated the relation between SVO and cooperative behavior. Such findings underscore the importance of personality in how people think about others, and ultimately behave, during interdependent situations. More work is needed on how SVO fits in the broader nomological network of personality constructs, and to what extent, if any, SVO can account for how broader personality constructs relate to social behavior.
SVO and trust. Expectations of others’ behavior in social dilemmas can be considered an operationalization of trust. Trust is often defined as a belief about another’s benevolent motive toward oneself (Balliet & Van Lange, 2013; Rousseau, Sitkin, Burt, &
Camerer, 1998). Indeed, if people expect others to cooperate in social dilemmas, this means they believe that the other person is willing to engage in costly behavior to provide them a benefit. So far, research on SVO and expectations has largely neglected to address the link between SVO and trust – it remains an open topic of research. Preliminary evidence indicates that prosocials tend to be more trusting than proselfs (Kanagaretnam, Mestelman, Nainar, &
Shehata, 2009), and that individuals scoring high on Honesty-Humility, a personality domain that shares significant overlap with SVO (Hilbig et al., 2014), are also more trusting toward others, but do not trust others unconditionally (Pfattheicher & Böhm, 2017). Nevertheless, there remains a need to generalize the SVO-expectation relation to how SVO relates to
61 SVO, EXPECTATIONS, AND COOPERATION various measures of state and trait trust. It may be that SVO is affecting variability in expectations of others’ behavior in social dilemmas, but not necessarily trust. That is, prosocial people may expect others to cooperate, but they believe that others are simply cooperating out of their own self-interest or for other reasons besides their internalized benevolent motives (e.g., the threat of being punished or a motive to maintain their reputation). It could also be that prosocial individuals are responding more strongly to or are even actively looking for cues that could be used to infer trust in others. For example, recent findings from an eye-tracking study indicate that deviations from a purely selfish value orientation (i.e., individualistic) predict how much attention is directed to searching for information about the other’s payoff in social dilemmas (Fiedler, Glöckner, Nicklisch, &
Dickert, 2013), and these differences in information search might generalize to other situations. The findings in the present meta-analysis underscore the need to further examine how SVO relates to trust.
Practical implications. The findings also emphasize several opportunities to strategically promote cooperation outside of the laboratory. Most of the empirical work on cooperation centers around how cooperation can be promoted to enhance solidarity and prosperity in and between societies. For example, SVO and trust have been studied as predictors of various organizational outcomes (Dirks & Ferrin, 2001), commuting preferences
(Van Lange et al., 1998; Van Vugt, Meertens, & Van Lange, 1995), and adherence to tax laws
(Van Dijke & Verboon, 2010). But in these situations, an enhanced threshold to cooperate exists because such cooperative behavior increases the risk of exploitation and abuse from others. One approach to promote cooperation is to reduce the conflict of interests and so align the goals of prosocials, individualists, and competitors (Smith, 1979). Yet, these structural changes to payoff matrices might not be easy or even impossible to implement. For example, most common resources, such as limited water supply in certain areas, cannot be equally split between all members of society due to practical or political limitations. In such situations, punishment for non-cooperation or reward for cooperation can increase expectations that
62 CHAPTER 2 others cooperate, and ultimately promote cooperative behavior (Balliet et al., 2011; Buckley,
Burns, & Meeker, 1974).
Another approach to promote cooperation in interdependent situations would be to ensure that individuals perceive that others are cooperating as well. If the expectation arises that others are cooperating, own cooperation becomes more likely. In addition, trust in others often supports one’s own goals, thereby reinforcing the influence of individual predispositions on behavior. Political messages or marketing campaigns, for example, should highlight the high percentage of individuals who already cooperate, instead of mentioning the percentage of individuals who do not cooperate. By enhancing perceived similarity among individuals, interpersonal trust and expectations of cooperation increase and reciprocal cooperation becomes more likely (Fischer et al., 2013). Such reciprocal cooperation can lead to substantial increases in collective action and in benefits for society as a whole (Fehr & Gächter, 2000).
Importantly, our findings suggest such appeals would affect everyone because once the expectation that others cooperate is elicited, it is associated with increased cooperation levels for prosocials and proselfs. Hence, the current findings may be used to promote any prosocial behavior, such as voting, recycling, volunteering, or donating to charities.
Another practical implication of the current findings pertains to partner selection.
Especially in dyadic contexts, individualists and competitors might often find their initial beliefs confirmed by eliciting selfish behavior from others through their own selfish actions
(Kelley & Stahelski, 1970; Miller & Holmes, 1975), leading prosocials to selectively interact with other cooperatively-minded individuals (Rand, Arbesman, & Christakis, 2011). Hence, our findings may generalize to partner selection because cooperative individuals are more likely to be selected as future social partners (Rockenbach & Milinski, 2011), and individuals who select their future partners might be largely guided by their beliefs about the other’s motives. As such, it is possible that prosocials only form lasting social relationship with other prosocials. However, it might also be that prosocials are initially more open to forming new relationships, whereas proselfs are more skeptical and reluctant, and only form lasting
63 SVO, EXPECTATIONS, AND COOPERATION relationships with others when additional information is available. The influence of cooperative preferences on partner selection promises to be another fruitful avenue for future research.
Limitations
The current meta-analysis is not without limitations. Despite strong theoretical and empirical reasons that support the hypothesis that expectations mediate the relation between
SVO and cooperation, the meta-analytic mediation model cannot support claims about causality. In fact, the position of all variables in the mediation model could be re-arranged and the outcome of the mediation model would remain unchanged. We did consider alternative models. For example, we did consider the possibility that cooperation mediates the relation between SVO and expectations (cf. self-justification; Messé & Sivacek, 1979), but did not find this model to be a viable alternative because of research showing that manipulated expectations result in increased levels of cooperation (e.g., Boone et al., 2008; Kuhlman &
Marshello, 1975), and because the correlation between expectations and cooperation is not stronger when expectations are assessed after versus before the measurement of cooperation
(Balliet & Van Lange, 2013). Another model would be that cooperation determines SVO.
However, this alternative is countered by the fact that SVO acts as a relatively stable personality characteristic (e.g., Van Lange, 1999; Van Lange et al., 2012) and most of the studies included in the meta-analysis (a) measure SVO before cooperation and (b) involved anonymous one-shot interactions. Therefore, based on existing theory and research we believe that the mediation model we present here is the most plausible model.
Future research could consider using an instrumental variable (IV; Angrist, Imbens, &
Rubin, 1996) to determine if expected partner cooperation has a causal effect on cooperation and mediates the relation between SVO and cooperation. IVs are used to determine causality, but a requirement of this method is that the IV only affects the dependent variable
(cooperation) through the mediating variable (expectations), but not directly. As such, IVs are usually hard to identify. Possibly, information about a partner’s past behavior toward others
64 CHAPTER 2
(i.e., reputational information) could serve as such an IV. Future research might examine if the effect of partner reputational information on own cooperation (J. Wu, Balliet, & Van
Lange, 2016) only occurs through expectations of partner cooperation, and then test the mediating effect of expectations on the relation between SVO and cooperation (also including the IV in the model). This approach could address another limitation of the current meta- analysis: the meta-analytic structural equation model was a just identified (saturated) path model, contained no degrees of freedom, and so we could not evaluate model fit. If an IV would be added to the model, then the direct path from this IV to cooperation could be omitted to gain one degree of freedom, allowing for model fit to be evaluated.
Conclusion
For nearly 50 years, theory and research on social dilemmas has devoted significant attention to SVO and expectations of cooperation as two important variables predicting cooperation in social dilemmas. The current meta-analysis is the first quantitative review of this literature that examines the interplay between both of these classic variables. We show that stable personality differences (i.e., SVO) predict expectations of cooperation, which in turn predict levels of cooperation in social dilemmas. Importantly, expectations are positively related to cooperation for both prosocials and proselfs. Thus, this meta-analysis helps to solve one puzzle of human cooperation. Although SVO and expectations of other’s cooperation exert independent influences on cooperation, we now have strong evidence that these variables are interrelated in shaping human cooperation, with expectations partially mediating the relation between SVO and cooperation.
65 SVO, EXPECTATIONS, AND COOPERATION
Footnotes
1 Although most research treats SVO as a stable dispositional personality construct, recent research has also considered how situations can activate state motives that are part of the SVO framework (e.g., Kelley et al., 2003; for a recent discussion on the state versus trait approach of SVO, see Ackermann, Fleiß, & Murphy, 2016; Pulford, Krockow, Colman, &
Lawrence, 2016).
2 Research in economics has developed and studied a related construct – conditional cooperation (Kocher, Cherry, Kroll, Netzer, & Sutter, 2008; Vollan & Ostrom, 2010).
3 The three models make predictions about how specific social dynamics and psychological processes affect how an individual’s own SVO relates to beliefs about the distribution of others’ SVO in a population. In the present meta-analysis, we examine how
SVO relates to beliefs about other’s cooperation in a social dilemma. Because both individualists and competitors tend to defect in social dilemmas, we cannot use these data to test how individualists and competitors differentially project their own SVO on others. For this reason, we cannot use these data to test different predictions from each of these three models. Instead, we examine the more general assertion that SVO should predict expectations of other’s cooperation in social dilemmas, and that these expectations can mediate the relation between SVO and cooperation.
4 A few studies (e.g., Haselhuhn, Wong, & Ormiston, 2013; Iedema & Poppe, 1995) assessed expectations of other’s behaviors in the same task used to measure SVO. These were excluded, because they increase the chance of common-method bias and because the SVO measures are not social dilemmas.
5 Professor Mike Cheung recommended in a personal consultation that all included studies should measure at least two of the three effect sizes of interest to ensure the validity of the MASEM approach used to examine this mediation.
66 CHAPTER 2
6 The effect size substantially increased after including studies that classified participants as prosocial or proselfs based on a goal choice in a social dilemma task, d =
0.644, 95% CI [0.516, 0.771], 90% prediction interval [-0.018, 1.306], p < .001.
7 We also examined moderators of the relation between SVO and cooperation. These moderator analyses can be found on the OSF webpage for this article.
8 For proselfs, the relation between expected partner cooperation and own cooperation may be stronger in iterated, compared to one-shot, social dilemmas, because cooperation can potentially maximize long-term outcomes during iterated interactions. However, for proselfs, the overall weighted effect size was actually significantly smaller in iterated (r = .439, k = 5,
95% CI [.218, .617], p < .001) than in one-shot social dilemmas (r = .650, k = 5, 95% CI
[.563, .723], p < .001), Q(1) = 4.393, p = .036. Yet, the number of iterations did not significantly moderate the relation between expectations and cooperation among proselfs (ß =
-0.015, p = .442). For prosocials, iterations did not moderate the relation between expectations and cooperation. The results of these analyses should be interpreted with caution due to low statistical power.
9 This statistical test contains only a few studies and has low statistical power.
67
68 CHAPTER 3
CHAPTER 3 SELFISHNESS FACILITATES DEVIANCE: THE LINK BETWEEN SOCIAL VALUE ORIENTATION AND DEVIANT BEHAVIOR
This chapter is based on Pletzer, J. L., Oostrom, J. K., & Voelpel, S. C. (2017). Selfishness facilitates deviance: The link between social value orientation and deviant behavior. Manuscript submitted for publication. Paper drafts have been presented at the WAOP Conference 2016 and the ENESER Small Group Meeting 2016.
69 SVO AND DEVIANCE
Abstract
Personality has long been acknowledged as an important predictor of norm-violating deviant behavior. The present study tested the relation between such behaviors and a narrow personality facet called social value orientation. In short, social value orientation describes individual differences in social preferences, and we hypothesized that individuals with selfish
(versus prosocial) orientations are more likely to violate norms and to act deviantly. Results of three studies (total N = 557) revealed that individuals who primarily focus on personal gains in an absolute or relative sense (i.e., individualists and competitors) report higher levels of workplace deviance (Studies 1, 2, and 3) and also act more deviantly (Studies 2 and 3) than those who value equality and collective outcomes (i.e., prosocials). A meta-analysis of the results across all three studies revealed a large difference in self-reported workplace deviance between prosocials and proselfs (d = 0.474). These results provide evidence for the utility of social value orientation in predicting and preventing deviant behavior. Organizations could therefore include a measure of these individual differences in their assessment procedure to screen applicants’ proneness to deviant behavior. Limitations and ideas for future research are discussed.
Keywords: Counterproductive work behavior, interpersonal, organizational, social value orientation
70 CHAPTER 3
Introduction
Norm violations are pervasive in human life. For example, research has shown that most individuals violate norms and rules when it benefits themselves (Ariely & Jones, 2012).
And while large scandals caused by norm violations, and by workplace deviance as one organizational form of such norm violations more specifically, of individuals in powerful positions regularly make headlines (e.g., Enron, Volkswagen, Uber), norm violations and deviant behavior of less powerful individuals can be equally costly for organizations and for society at large. For example, a plethora of research has shown that workplace deviance (e.g., fraud, wasting time, etc.) is an omnipresent and expensive problem for organizations (e.g.,
Dunlop & Lee, 2004; Murphy, 1993); it can lead to higher turnover rates, bankruptcy, an impaired business reputation, and to decreased task performance (e.g., Kaptein, 2008; Levy &
Tziner, 2011; Sackett, 2002). Given these huge costs, improving the prediction and prevention of such deviant behaviors is an important priority for research and practice.
In fact, contemporary social, personality, and especially organizational psychology research has contributed to a growing body of research about the precursors and causes of workplace deviance (e.g., Berry, Ones, & Sackett, 2007). For example, leadership behavior and styles (e.g., Mitchell & Ambrose, 2007; Resick, Hargis, Shao, & Dust, 2013; Tepper et al., 2009), demographic characteristics (e.g., Ng, Lam, & Feldman, 2016), and employee personality (e.g., De Vries & Van Gelder, 2015; O’Neill & Hastings, 2011) have been shown to be related to levels of workplace deviance in an organization. Personality is an important predictor of workplace deviance, but relying only on broad personality dimensions is not optimal when predicting workplace deviance for a number of reasons, including increased efficiency in predicting criteria and higher conceptual resemblance with criteria (e.g.,
Hastings & O’Neill, 2009). Hence, the goal of the present study is to advance the prediction and prevention of norm violations, and especially of workplace deviance, by introducing a narrow personality characteristic that is novel in the prediction of deviant behavior (i.e., social
71 SVO AND DEVIANCE value orientation (SVO)). The present study thereby contributes to current knowledge on how individual differences help to predict and prevent the occurrence of deviant behavior.
Workplace Deviance
One common norm violation is workplace deviance, which is often also referred to as counterproductive work behavior and which is defined as voluntary acts by employees that violate organizational norms and thereby harm the wellbeing of the organization or its employees (Robinson & Bennett, 1995). Workplace deviance can vary from minor (e.g., arriving too late to work, littering) to severe (e.g., fraud, theft), and two forms are commonly distinguished: interpersonal and organizational workplace deviance. Interpersonal workplace deviance describes behaviors that are harmful to other individuals in the organization (e.g., insulting colleagues), whereas organizational workplace deviance describes behaviors that directly harm the organization (e.g., fraud; Bennett & Robinson, 2000). According to Kish-
Gephart, Harrison, and Treviño (2010), workplace deviance has three antecedents: the organizational environment, the issue itself, and stable individual differences. The present study focuses on the latter, contending that organizations and society at large would fare better if they were to understand and be aware of individual differences which incline people away from detrimental behaviors that impinge upon the success and cohesion of these organizations and of society (Dunlop & Lee, 2004).
In employee selection contexts, personality is the most commonly assessed individual difference (Ryan et al., 2015). Broad personality factors (e.g., Big Five Conscientiousness or
Agreeableness) correlate moderately with workplace deviance (Berry et al., 2007; Salgado,
2002). Yet, recent research has revealed that the criterion-related validity of personality can be increased by focusing on narrow facets rather than broad personality factors (e.g., Ashton,
Paunonen, & Lee, 2014; Pomerance & Converse, 2014). One reason for the less than optimal criterion-related validity of global personality measures is that the underlying facets suppress each other’s effects on the criterion (Hastings & O’Neill, 2009). Ashton and colleagues
(2014) also argue that due to the higher conceptual correspondence between certain
72 CHAPTER 3 personality facets and specific criteria, such as workplace deviance, an exclusive reliance on global personality factors can be counterproductive. In addition to the higher effectiveness and efficiency in predicting criteria, Hastings and O’Neill (2009) conclude that facet-level measurement of personality characteristics is more defensible in employment decisions because it signals applicants that tests are relevant to the job in question. A narrow focus on deviant-related personality facets therefore seems to be warranted and desirable (O’Neill &
Hastings, 2011). While narrow facets from the predominant personality models have already been investigated as predictors of norm-violating deviant behavior (e.g., anger from the Big
Five; Hastings & O’Neill, 2009), we believe that SVO is a novel and promising personality facet that might further contribute to the understanding and prediction of such behavior.
Social Value Orientation
Research on SVO is based on experimental games and challenged the widely held belief that humans only make decisions according to selfish principles based on economic rationality (e.g., Luce & Raiffa, 1957) by showing that some individuals do not just pay attention to their own outcomes in interdependent situations, but also to the outcomes of others (Messick & McClintock, 1968). Building on this, SVO can be assumed to reflect an individual’s sense of fairness and equality in interdependent situations and is defined as a stable personality characteristic which describes the weights individuals attach to their own and others’ outcome in interdependent situations (McClintock, 1972).
SVO is commonly measured with either the Triple Dominance Measure (Van Lange,
Otten, De Bruin, & Joireman, 1997), or with the SVO Slider Measure (Murphy, Ackermann,
& Handgraaf, 2010).1 In these tasks, participants are asked to allocate resources (i.e., points) between themselves and a hypothetical person. Based on their decisions, individuals are classified into one of three different SVOs: 1) prosocials, who maximize or equalize joint outcomes; 2) individualists, who maximize their own outcome regardless of the other’s outcome; and 3) competitors, who maximize the difference between their own and the other’s outcome. Individualists and competitors are often combined to form a proself group (e.g., Van
73 SVO AND DEVIANCE
Lange & Liebrand, 1989). Using the SVO Slider measure, SVO can also be measured on a continuum, with higher scores indicating higher levels of prosociality. Within the broader personality frameworks, SVO correlates most strongly with HEXACO Honesty-Humility and with Big Five Agreeableness (Hilbig et al., 2014).
Individual differences in SVO have reliably been shown to predict decision-making in situations in which the outcomes of two or more individuals depend on the actions of all individuals involved (i.e., in social dilemmas), indicating that prosocials cooperate more with others than individualists or competitors (Balliet et al., 2009; Pletzer et al., 2018). Previous research has also demonstrated the ecological validity of SVO by showing that prosocials are more likely to engage in environmentally friendly behavior (e.g., Cameron, Brown, &
Chapman, 1998; Joireman, Lasane, Bennett, Richards, & Solaimani, 2001), donate more to noble causes (e.g., McClintock & Allison, 1989; Van Andel, Tybur, & Van Lange, 2016), are more likely to volunteer (e.g., Van Lange, Schippers, & Balliet, 2011), and have also been shown to be more concerned with the goals of other departments at work (e.g., Nauta, De
Dreu, & Van Der Vaart, 2002).
Social Value Orientation and Workplace Deviance
The occurrence of workplace deviance can be best explained with social exchange theory (e.g., Cook & Rice, 2003; Cropanzano & Mitchell, 2005; Emerson, 1976). Social exchange theory is based on the premise that human interactions are built upon the contingent exchange of material and social resources and posits that a subjective cost-benefit analysis governs the formation of human relationships (e.g., Cook & Rice, 2003; Cropanzano &
Mitchell, 2005; Emerson, 1976). In a social context, individuals engage voluntarily in interactions with others from which they then expect returns. As such, employees clearly have social exchange relationships with their coworkers and supervisors at work: information, help, or even a sense of belonging are resources exchanged on an interpersonal level at work.
However, an exchange relationship can also exist with organizations or employers: employees receive compensation or bonuses in exchange for performance at work. In addition,
74 CHAPTER 3 employees with a high quality social exchange relationship with their employer have been found to exhibit more pro-organizational behavior and show higher levels of mutual respect, trust, and job satisfaction (Stamper, Masterson, & Knapp, 2009). Importantly, in such cases, the occurrence of workplace deviance can be conceptualized as employees’ violation of social exchange norms in their relationship with coworkers and supervisors or with the organization.
The question that then arises is which individual differences incline individuals to be more likely to violate these norms?
Here, we propose that individual differences in SVO influence the likelihood that individuals violate these social exchange norms. Prosocials, who per definition value equality and fairness in outcomes, should perceive deviant behaviors as unacceptable because these violate their inherent fairness perceptions and disrupt the contingent, positive exchange of resources and rewards. Prosocials should therefore largely refrain from acting deviantly.
Proselfs, who pursue relative or absolute gains compared to others, do not approach situations with fairness and equality in mind (Van Lange, 1999) , and should therefore not perceive deviant behavior as disrupting fair social exchange processes. Acting deviantly might be in their best interest. For example, whereas prosocials’ internalized fairness values should make them aware that coming too late to work harms coworkers and the organization, proselfs simply focus on their own resource gain (i.e., individualists) or might even want to actively hurt their organization by engaging in deviant behavior if this were to serve their own goals
(i.e., competitors). Based on this reasoning, we expect proselfs to behave more deviantly at work than prosocials.
In line with this theory-based expectation, some evidence suggests that SVO predicts workplace behaviors. Van Dijk and De Cremer (2006) showed that proself leaders made more self-beneficial decisions than prosocial leaders. In addition, prosocials have been found to be better at problem solving due to their increased interdepartmental cooperation (Nauta et al.,
2002) and their greater tendency to use cooperative heuristics in negotiations than proselfs
(De Dreu & Boles, 1998). Prior research also indicates that prosocials use less strategic
75 SVO AND DEVIANCE misrepresentation and lying when making decisions that influence their own and another’s outcome (Steinel & De Dreu, 2004). Peterson (2002) also found that organizations suffer from higher levels of workplace deviance when employees are primarily concerned with their own outcomes, and Hastings and O’Neill (2009) demonstrated that the narrow personality trait of cooperation negatively predicts workplace deviance. In addition, among the broad personality traits, HEXACO Honesty-Humility is the strongest predictor of levels of workplace deviance (Lee, Ashton, & De Vries, 2005), which itself is predictive of choices on the SVO measures (Hilbig et al., 2014). Based on social exchange theory and the above findings on SVO in the workplace, the following hypothesis is formulated:
Hypothesis 1: Proselfs report higher levels of workplace deviance than prosocials.
As mentioned above, SVO is highly predictive of behavior in situations in which outcomes depend on the actions of two or more involved individuals (i.e., social dilemmas;
Balliet et al., 2009). Prosocials value fairness and equality in outcomes, whereas selfish individuals either do not care about the other individuals involved (i.e., individualists) or actively want to harm them (i.e., competitors). Because SVO is an inherent social construct measuring behavioral tendencies and goals in interdependent social situations (Van Lange,
1999), we expect that the effect on interpersonal, as opposed to organizational, workplace deviance to be even larger when comparing prosocials and proselfs. In addition, the social exchange relationship between employees and their coworkers or supervisors should be influenced more strongly by individual differences in SVO than the social exchange relationship between employees and their organization because such interpersonal relationships might be more salient in everyday working life. Thus, the following hypothesis is formulated:
Hypothesis 2: The effect size will be larger when comparing prosocials’ and proselfs’ levels of interpersonal workplace deviance than when comparing their levels of organizational workplace deviance.
Study Overview
76 CHAPTER 3
The goal of the present study is to introduce the personality characteristic of SVO as a predictor of norm-violating deviant behaviors and to test its predictive value in three studies.
In Study 1, we examine the SVO’s predictive validity for self-reported workplace deviance.
In Study 2, we again test SVO’s predictive validity but use a different measure and include a behavioral measure of deviance. In addition, we examine the incremental predictive validity of SVO over and above the broad personality dimension that predicts workplace deviance most strongly (i.e., HEXACO Honesty-Humility). Finally, in Study 3 we seek to corroborate the findings of Studies 1 and 2 regarding self-reported workplace deviance and use a different behavioral measure of deviance to further substantiate our findings.
Method Study 1
Participants and Procedure
A total of 180 participants were recruited online via social media. To avoid social desirable responding, participation was voluntary and not compensated. Participants whose
SVO could not be classified (n = 34) and who showed a repetitive pattern in their responses
(i.e., always responding with a 5; n = 2) were excluded, resulting in a final sample of 144 participants (65% female; due to technical difficulties, this percentage only represents 35 of the 144 participants). All participants were currently employed and held a wide variety of jobs at various levels, ranging from a cashier to an engineer. On average, they were 28.48 years old (SD = 9.88), worked 37.84 hours (SD = 17.31) per week, had been employed for 8.94 years (SD = 9.43), and had held their current job for 4.35 years (SD = 5.95). Participants were assured that all responses would be treated anonymously and confidentially, and provided informed consent prior to participation and were debriefed afterwards. Ethical approval for all studies was obtained from the first author’s university’s ethical review board.
Measures
Social Value Orientation. Participants’ SVO was assessed with the Triple
Dominance Measure (TDM; Van Lange, Otten, De Bruin, & Joireman, 1997). For nine items, participants had to choose between three outcome distributions of points for the self and for a
77 SVO AND DEVIANCE hypothetical other, representing the three major SVOs. They were instructed to choose the option they prefer the most. Participants were classified as prosocials (n = 81), individualists
(n = 42), or competitors (n = 21) if they made six consistent choices (n = 34 unclassified).
Individualists and competitors were combined to form a proself group (n = 63). Being proself was coded as 0, prosocial as 1.
Workplace deviance. Bennett and Robinson's (2000) 19-item questionnaire was used to measure self-reported workplace deviance. While some studies assess workplace deviance with supervisor or colleague ratings (e.g., Neves & Story, 2015), the use of a self-report measure is in line with the argument that workplace deviance is usually a hidden behavior of which supervisors and colleagues are not aware (Berry et al., 2012; Jones, 2009) (Berry et al.,
2007; Jones, 2009). In addition, previous research has shown that self-reports measure organizational behaviors accurately (Spector, 1994) and that workplace deviance can be validly assessed with a self-report measure when participants are guaranteed anonymity and confidentiality of their responses (e.g., Bennett & Robinson, 2000). Participants indicated how often they engaged in certain deviant behaviors during the past year on a scale ranging from 1 = never to 7 = daily (α = .87). The questionnaire consists of two subscales measuring interpersonal (7 items; α = .85) and organizational workplace deviance (12 items; α = .81). A sample item of interpersonal workplace deviance is “Made fun of someone at work” and
“Taken property without permission” of organizational workplace deviance. We ran a confirmatory factor analysis (CFA; N = 178) to test the two-factor structure of workplace deviance against a one-factor structure. The two-factor structure of workplace deviance (CFI
= .788; TLI = .760; RMSEA = .092; SRMR = .085; �2 = 378.47, p < .001) fit the data better than the one-factor structure (CFI = .634; TLI = .589; RMSEA = .120; SRMR = .104; �2 =
544.09, p < .001). A maximum likelihood ratio difference test for �2 showed a highly significant difference between the one- and the two-factor structure (Δ�2 = 165.62, p < .001), indicating that a two-factor representation of workplace deviance is preferred above a one- factor representation.2
78 CHAPTER 3
Results and Discussion Study 1
Because both workplace deviance and SVO have reliably been shown to be influenced by participants’ age (N. Liu & Ding, 2012; Pletzer, Oostrom, & Voelpel, 2017; Shao, Resick,
& Hargis, 2011), we controlled for age in all analyses. A one-way analysis of covariance
(ANCOVA) shows a statistically significant difference in self-reported workplace deviance between prosocials and proselfs when controlling for age, F(1, 141) = 8.925, p < .01, R2 =
.106. The significant effect is found for both interpersonal, F(1, 141) = 8.002, p < .01, R2 =
.094, and organizational workplace deviance, F(1, 141) = 5.379, p = .022, R2 = .068. The estimated marginal means reveal that proselfs indicated to behave more deviantly on the overall scale and on both subscales (see Figure 1). Hypothesis 1 can therefore be confirmed.
In addition, unclassified individuals (n = 34) take an intermediate position (estimated marginal mean: M = 2.11, SE = 0.14) and both pairwise comparisons with prosocials (p =
.152) and proselfs (p = .421) are non-significant, providing further evidence that SVO drives the difference in self-reported workplace deviance. The effect size comparing prosocials and proselfs was larger for interpersonal (R2 = .094) than for organizational (R2 = .068) workplace deviance, providing initial support for Hypothesis 2.
79 SVO AND DEVIANCE
Figure 1 Estimated Marginal Means (controlling for age) for Overall, Interpersonal, and Organizational Workplace Deviance for Prosocials and Proselfs for Study 1, 2, and 3. 3
2,5
2 Prosocial Proself
1,5
1 WD ID OD WD ID OD WD ID OD
Study 1 Study 2 Study 3 Note. WD = Overall workplace deviance; ID = Interpersonal workplace deviance; OD = Organizational workplace deviance.
The results of Study 1 indicate that SVO is associated with self-reported workplace deviance and provide preliminary evidence that organizations would benefit from hiring individuals who are predisposed to cooperate with each other and who value fairness and equality in outcomes to avoid the occurrence of workplace deviance. However, the first study suffers from two limitations. First, while the TDM is the most commonly used SVO measure, we had to exclude participants because their SVO could not be classified. In Study 2, we want to overcome this issue by using a newer SVO measure (i.e., the SVO Slider measure; Murphy et al., 2010) which can classify all participants and will provide a continuous measure of
SVO. Second, workplace deviance was only assessed with self-reports. Even though self- reports of workplace deviance seem to validly assess the phenomenon of interest (Bennett &
Robinson, 2000; Spector, 1994), we cannot rule out that the findings for self-reported workplace deviance are due to a response bias unique to either prosocials or proselfs. We will therefore replicate and extend this study by including a behavioral measure of deviance
(Fischbacher & Föllmi-Heusi, 2013) to overcome the limitation of common method bias and
80 CHAPTER 3 of obtaining only subjective self-report data. More specifically, we will examine the relationship between SVO and a behavioral measure of norm-violating deviant behavior in which participants can increase their gain by disobeying instructions (i.e., by acting deviantly). In the second study, we aim to corroborate Hypotheses 1 and 2 and additionally hypothesize the following:
Hypothesis 3: Proselfs behave more deviantly than prosocials.
Furthermore, we will examine if SVO explains incremental variance in workplace deviance over and above HEXACO Honesty-Humility, which is the strongest predictor of workplace deviance out of all Big Five and HEXACO broad personality factors (Lee et al.,
2005). As mentioned above, SVO is a narrow personality characteristic, which generally enjoy a few advantages over broad personality dimensions in the prediction of deviant behavior (see the introduction for an elaborate discussion of these advantages). However, these advantages are only practically useful if SVO explains incremental variance in workplace deviance over and above Honesty-Humility. We investigate this in Study 2.
Method Study 2
Participants and Procedure
A total of 331 participants completed an online self-report questionnaire and participated in a behavioral measure of deviance in exchange for $1. They were recruited online through the crowdsourcing platform CrowdFlower, were guaranteed anonymity and confidentiality of their responses, provided informed consent prior to participation, and were debriefed afterwards. Because we used a different SVO measure than in Study 1, no participants had to be excluded because their SVO could not be classified (Murphy,
Ackermann, & Handgraaf, 2010). However, 59 participants were excluded because they did not answer attention test questions correctly (i.e., “Please select daily on this question”). The final sample therefore consisted of 272 participants (45% female). On average, participants were 35.33 years old (SD = 11.29), worked 36.23 hours per week (SD = 12.87), had been
81 SVO AND DEVIANCE employed for 12.37 years (SD = 10.68), and had held their current jobs for 5.62 years (SD =
5.27).
Measures
Social Value Orientation. Participants’ SVO was assessed with the online version of the SVO Slider measure (Murphy et al., 2010). Six items posed a resource allocation task in which participants had to make a choice between a payoff for themselves and another person on a defined continuum. Participants were instructed to choose the option that best represents their preferences for joint distribution of outcomes and were classified as prosocials (n = 155), individualists (n = 111), or competitors (n = 6). Individualists and competitors were again combined to form a proself group (n = 117). Using the Slider measure, SVO can also be treated as a continuous variable (higher values indicating more prosocial values, which aligns with the categorical SVO coding: 0 = proself, 1 = prosocial).
Workplace deviance. The same measure as in Study 1 was used (Bennett &
Robinson, 2000). Alpha coefficients of the overall scale (α = .96) as well as of the organizational (α = .95) and interpersonal workplace deviance scale (α = .93) were substantial. A CFA for the proposed two-factor structure of workplace deviance (CFI = .881;
TLI = .866; RMSEA = .120; SRMR = .065; �2 = 740.70, p < .001) fit the data better than for a one-factor solution of workplace deviance (CFI = .865; TLI = .849; RMSEA = .127; SRMR =
.069; �2 = 821.68, p < .001). A maximum likelihood ratio difference test for �2 between the two estimations was highly significant (Δ�2 difference = 80.98, p < .001), again favoring the two-factor solution.
HEXACO Honesty-Humility. We assessed Honesty-Humility as the strongest predictor of workplace deviance with 16 items from the 100 items HEXACO questionnaire
(Lee & Ashton, 2016). Example items are “I would never accept a bribe, even it were very large” or “I am an ordinary person who is not better than others.” Cronbach’s alpha was substantial (α = .84). Participants indicated their response on a five-point scale ranging from 1
= strongly disagree to 7 = strongly agree.
82 CHAPTER 3
Behavioral measure of deviance. To measure deviant behavior, we used an adapted version of the die-rolling task (Fischbacher & Föllmi-Heusi, 2013). Instead of receiving a cup and a die, participants were forwarded to an external website (random.org) where they could roll a virtual die. Participants could roll the die as often as they wanted to, but were explicitly instructed to roll it only once. They were told that if they rolled a six, their compensation would be doubled. A previous study using the same die-rolling task showed that cheating occurs by rolling the die repeatedly (Köbis, Van Prooijen, Righetti, & Van Lange, 2016).
Therefore, unobtrusively to participants, we recorded how often they had rolled the die and operationalized deviant behavior as the number of times participants rolled the die because this constitutes disobeying instructions. Higher numbers of rolling the die indicate higher levels of cheating because instructions are disobeyed multiple times. Overall, participants rolled the die on average 3.55 times (SD = 5.59), ranging from 1 roll to 37 rolls.
Results and Discussion Study 2
Using the SVO Slider measure, it is possible to analyze the data using both a continuous and categorical measure of SVO. Hence, we tested the relationship between SVO and workplace deviance with both an ANCOVA and a linear regression, controlling for age in both analyses. The correlation analyses indicate that the continuous and categorical SVO variable significantly and negatively correlated with overall, organizational, and interpersonal workplace deviance (see Table 1).
83 SVO AND DEVIANCE
Table 1 Descriptive Statistics and Bivariate Correlations of Study 2 Mean SD 1. 2. 3. 4. 5. 6. 7. 1. SVO cont. 23.49 15.43 - 2. SVO cat. 0.43 0.50 .893** - 3. WD 2.40 1.30 -.300** -.247** - 4. OD 2.42 1.33 -.262** -.210** .977** - 5. ID 2.37 1.39 -.331** -.283** .937** .840** - 6. Die throws 3.55 5.59 -.103 -.136* .099 .066 .143* - 7. Age 35.33 11.29 .109 .080 -.176** -.150* -.200* -.088 - 8. HH 3.35 0.58 .324** -.324** -.501** -.463** -.514** -.153* .327** Note. N = 272. SVO cont. = Degree on the SVO Slider measure; SVO cat = SVO categorization (1 = prosocial, 0 = proself); WD = Workplace deviance; OD = Organizational workplace deviance; ID = Interpersonal workplace deviance; HH = HEXACO Honesty- Humility; * p < .05, ** p < .01.
The one-way ANCOVA controlling for age also showed a statistically significant difference in self-reported workplace deviance between prosocials and proselfs, F(1, 269) =
16.089, p < .01, R2 = .086. These differences were significant for both organizational, F(1,
269) = 11.348, p < .01, R2 = .062, and interpersonal workplace deviance, F(1, 269) = 21.648, p < .01, R2 = .112. The estimated marginal means of workplace deviance are consistently higher for proselfs than for prosocials and are generally higher than in Study 1 (see Figure 1).3
Similarly, the linear regression analysis revealed a significant effect of SVO on overall workplace deviance, F(2, 269) = 16.723, p < .01, R2 = .111, as well as on organizational, F(2,
269) = 12.279, p < .01, R2 = .084, and interpersonal workplace deviance, F(2, 269) = 21.317, p < .01, R2 = .137. The regression coefficients indicate that individuals with a relatively more prosocial SVO show lower levels of workplace deviance (see Table 2). Hypothesis 1 is thus again supported.
Providing further preliminary support for Hypothesis 2, the effect sizes were larger for interpersonal (R2 = .112 and R2 = .137) than for organizational workplace deviance (R2 = .062 and R2 = .084), independent of the analytical method.
A one-way ANCOVA predicting behavioral deviance in the die game controlling for age found a statistically significant difference between prosocials and proselfs, F(1, 269) =
4.593, p = .033, R2 = .024, indicating that proselfs (estimated marginal mean: M = 4.38, SE =
0.51) rolled the die more often than prosocials (estimated marginal mean: M = 2.92, SE =
84 CHAPTER 3
0.45). The results of the linear regression analysis were nonsignificant, F(2, 269) = 2.264, p =
.106, R2 = .017 (see Table 2 for the regression coefficients). Hence, results regarding
Hypothesis 3 are equivocal depending on the analytical method.
Table 2 Linear Regression Analyses of Study 2 DV WD OD ID Die Throws IV R2 β R2 β R2 β R2 β .111 .084 .137 .017 SVO cont. -.024** -0.21** -.028** -.034 Age -.017* -0.14* -.021* -.038 Note. N = 272; DV = Dependent variable; IV = Independent variable; SVO cont. = Degree on the SVO Slider measure; SVO cat = SVO categorization (1 = prosocial, 0 = proself); WD = Workplace deviance; OD = Organizational workplace deviance; ID = Interpersonal workplace deviance; β = Standardized Beta Coefficient; * p < .05, ** p < .01.
Lastly, we examined if SVO explains incremental variance in workplace deviance over and above HEXACO Honesty-Humility. A stepwise linear regression predicting overall workplace deviance with Honesty-Humility and SVO as independent variables and controlling for age was highly significant, F(3, 268) = 33.456, p < .01, R2 = .272. SVO explained 2.1% of additional variance in workplace deviance over and above Honesty-
Humility. The results were qualitatively similar for organizational workplace deviance, F(3,
268) = 26.391, p < .01, R2 = .228, and for interpersonal workplace deviance, F(3, 268) =
37.458, p < .01, R2 = .295. SVO explained 1.4% of additional variance over and above
Honesty-Humility for organizational workplace deviance and 3.0% for interpersonal workplace deviance. A stepwise linear regression predicting behavioral deviance in the die rolling game with Honesty Humility and SVO controlling for age was marginally nonsignificant, F(3, 268) = 2.588, p = .028, R2 = .295. SVO did not explain incremental variance over and above Honesty-Humility.
The results of Study 2 corroborate our findings from Study 1: proselfs consistently report to behave more deviantly in the workplace than prosocials. Importantly, these findings hold even though we used a different measure for SVO than in Study 1 and paid individuals for their participation. Furthermore, the results remain statistically significant independent of
85 SVO AND DEVIANCE whether a categorical or continuous measure of SVO is used, increasing the generalizability of our findings even more. The results also indicate that SVO explains variance in workplace deviance over and above the broad personality dimension of Honesty-Humility, which has been shown to be the strongest predictor of workplace deviance across all Big Five and
HEXACO personality domain scales (Lee et al., 2005). This suggests that SVO is a narrow personality facet that helps to overcome the limitations associated with only relying on broad personality dimensions as predictors of workplace deviance, such as the suppressed effects of individual facets (Hastings & O’Neill, 2009), higher conceptual resemblance with criteria
(Ashton, Paunonen, et al., 2014), and the higher criterion-related validity in job selection contexts (Ashton, Paunonen, et al., 2014; Hastings & O’Neill, 2009; Pomerance & Converse,
2014). In addition, we corroborated the finding that proselfs report to be more deviant than prosocials with a behavioral measure of deviance when using the categorical classification of
SVO. Here as well, proselfs deviated more from the given instructions than prosocials. This finding makes it unlikely that proselfs’ self-reported levels of workplace deviance are due to a response bias unique to this group. However, these conclusions might be questioned when examining the results using the continuous SVO measure because they were nonsignificant.
We also acknowledge that the die-rolling task we used in this study has a limitation: Although previous studies using the same task showed that cheating occurs by repeatedly rolling the die and not by lying about the outcome (Köbis et al., 2016), some participants might have rolled the die multiple times for other motives than cheating (e.g., out of curiosity or to check if the die was truly random). In addition, it is possible that some individuals who would usually act deviantly rolled a six on their first try, negating the necessity to be deviant. This might have confounded the results. Although we believe that this task is closely aligned with an important deviant behavior at work (i.e., disobeying instructions from supervisors) and the operationalization of deviant behavior at least correlated significantly with self-reported interpersonal workplace deviance (r = .143, p = .019), a more comprehensive task would ideally capture evidence of behavior that is more broadly related to the workplace. We
86 CHAPTER 3 therefore conducted a third study using a different behavioral measure of deviance to clarify the relation between SVO and behavioral differences in norm-violating deviance. By using a matrix task modelled after Mazar, Amir, and Ariely (2008) in which participants could increase their outcomes by lying about their performance, we aim to measure not only deviant selfish behavior, but also an overrepresentation of one’s actual performance and dishonesty toward individuals in an authoritative position.
Method Study 3
Participants and Procedure
A total of 141 students (63% female) participated in this study for credits or for €5 in the lab. They could increase their compensation in the matrix task described below. One student had to be excluded from all analyses because he did not indicate his age. All participants provided informed consent before participation and were debriefed afterwards.
Participants were on average 21.92 years old (SD = 4.43), worked 12.40 hours per week (SD
= 8.28), and had been employed for an average of 4.22 years (SD = 2.96).
Measures
Social Value Orientation. As in Study 2, participants’ SVO was assessed with the online version of the SVO Slider measure (Murphy et al., 2010). Participants were classified as prosocials (n = 95) or individualists (n = 45), but no one was classified as a competitor.
Therefore, all analyses are based on the comparison between prosocials and individualists, or on the continuous SVO variable.
Workplace deviance. The same measure as in Study 1 and 2 was used to assess self- reported levels of workplace deviance (Bennett & Robinson, 2000). Alpha coefficients of the overall scale (α = .79) as well as of the organizational (α = .72) and interpersonal workplace deviance scale (α = .80) were acceptable. A CFA for a two-factor structure of workplace deviance fit the data better (CFI = .749; TLI = .716; RMSEA = .082; SRMR = .086; �2 =
293.12, p < .001) than a one-factor structure (CFI = .575; TLI = .522; RMSEA = .106; SRMR
= .106; �2 = 393.09, p < .001). A maximum likelihood ratio difference test for �2 between the
87 SVO AND DEVIANCE two estimations was highly significant (Δ�2 = 99.97, p < .001), indicating that the two-factor solution of workplace deviance fit the data better.
Behavioral measure of deviance. We used a matrix task modelled after Mazar and colleagues (2008) to assess deviance and dishonesty. In this task, participants received a test and a response sheet. The test sheet consisted of 20 matrices each containing 12 three-digit numbers (i.e., 0.85, 7.34) between zero and ten. Participants had five minutes to find the two numbers that add up to exactly 10 in each matrix and received €0.50 for each solved matrix.
Yet, only 10 of the 20 matrices were solvable. After five minutes, participants were instructed to throw the test sheet in a trash can, to indicate on the response sheet how many matrices they had solved, and to hand the response sheet to the experimenter. This presented them with the opportunity to lie about the number of matrices they had solved. Participants were paid in accordance with the number of solved matrixes they had indicated on the response sheet.
After they had left, the experimenter retrieved the test sheet from the trash can and compared the reported with the actual number of solved matrices. The difference between the two was operationalized as the measure of norm-violating deviance. Five participants were excluded from this analysis because their test sheet could not be retrieved.
Results Study 3
A one-way ANCOVA controlling for age showed that prosocials and individualists marginally did not differ on self-reported workplace deviance when controlling for age, F(1,
137) = 3.762, p = .054, R2 = .043. The difference was statistically significant for organizational workplace deviance, F(1, 137) = 8.435, p < .01, R2 = .063, but not for interpersonal workplace deviance, F(1, 137) = 0.020, p = .889, R2 = .024. The estimated marginal means were higher for individualists than for prosocials for both overall and organizational workplace deviance, but not for interpersonal workplace deviance (see Figure
1). A linear regression showed similar results: SVO significantly predicted overall workplace deviance, F(2, 137) = 3.270, p = .041, R2 = .046, and organizational workplace deviance, F(2,
137) = 4.659, p = .011, R2 = .064, but not interpersonal workplace deviance, F(2, 137) =
88 CHAPTER 3
1.700, p = .187, R2 = .024. As in Study 2, individuals with a relatively more prosocial SVO were less likely to report (overall and organizational) workplace deviance (see Table 3 for correlations and Table 4 for regression coefficients).
Table 3 Descriptive Statistics and Bivariate Correlations of Study 3 Mean SD 1. 2. 3. 4. 5. 6. 1. SVO cont. 27.53 30.84 - 2. SVO cat. 0.68 0.50 .854** - 3. WD 2.09 0.64 -.158 -.140 - 4. OD 1.84 0.91 -.236** -.227** .873** - 5. ID 2.24 0.71 .013 .035 .751** .334** - 6. Matrix 0.68 2.43 .147 -.176* .023 .085 -.074 - 7. Age 21.92 4.43 -.073 -.151 -.131 -.072 -.156 .245** Note. N = 140 (Matrices N = 136). SVO cont. = Degree on the SVO Slider measure; SVO cat = SVO categorization (1 = prosocial, 0 = proself); WD = workplace deviance; OD = Organizational workplace deviance; ID = Interpersonal workplace deviance; Matrix = Reported – solved number of matrices; * p < .05, ** p < .01.
Table 4 Linear Regression Analyses of Study 3 DV WD OD ID Matrices IV R2 β R2 β R2 β R2 β .046 .064 .024 .080 - SVO cont. -0.17* 0.00 -0.14 0.24** Age -0.14 -0.09 -.016 0.24** Note. N = 140 (Matrices N = 136); DV = Dependent Variable; IV = Independent Variable; SVO cont. = Degree on the SVO Slider measure; SVO cat = SVO categorization (1 = prosocial, 0 = proself); WD = Workplace deviance; OD = Organizational workplace deviance; ID = Interpersonal workplace deviance; β = Standardized Beta Coefficient; * p < .05, ** p < .01.
A one-way ANCOVA (controlling for age) predicting behavioral deviance in the matrix task showed that prosocials and proselfs marginally did not differ, F(1, 133) = 3.413, p
= .067, R2 = .084. The estimated marginal means reveal that proselfs’ levels of deviance were higher than prosocials’ (proself M = 1.55, SE = 0.38; prosocial M = 0.69, SE = 0.27). The results of the linear regression analysis were statistically significant, F(2, 133) = 5.780, p <
.01, R2 = .080 (see Table 4 for the regression coefficients), indicating that proselfs behaved more deviantly than prosocials.
89 SVO AND DEVIANCE
Meta-Analysis of Studies 1 through 3
To summarize the findings and provide an overview of the results across three studies, we conducted a fixed-effects meta-analysis using the estimated marginal means of workplace deviance for prosocials and proselfs from the ANCOVAs when controlling for age to examine the generality of our findings. Across three studies, proselfs (n = 225) reported higher levels of organizational (d = 0.433, 95% CI [0.261, 0.605], p < .001), interpersonal (d = 0.405, 95%
CI [0.232, 0.577], p < .001), and overall workplace deviance (d = 0.463, 95% CI [0.291,
0.636], p < .001) than prosocials (n = 331). Together, these meta-analytic results provide further support for Hypothesis 1. However, across three studies the effect size was not larger for interpersonal than for organizational workplace deviance, Q(1) = 0.053, p = .818.4 Hence,
Hypothesis 2 was not supported.
General Discussion
The goal of the present study was to test the predictive validity of SVO for norm- violating deviant behaviors. Across three studies, we demonstrated that individual differences in SVO are associated with norm-violating deviant behavior: proselfs report higher levels of workplace deviance and act more deviantly than prosocials. The strength of this effect is exemplified by the fact that it holds in three independent studies which differed in terms of participant payment (unpaid vs. paid), demographic and work-related characteristics
(percentage of females, mean age, working hours, and work experience), and measurement methods. The current results emphasize several new insights for the prediction and prevention of norm-violating deviant behavior.
Using social exchange theory (Cook & Rice, 2003; Cropanzano & Mitchell, 2005;
Emerson, 1976), we argued that deviant behaviors can be understood as violating social exchange norms and that selfish dispositions are associated with a higher likelihood of disrupting those norms. Selfish individuals would therefore be more inclined to act deviantly.
The current studies demonstrate that individual differences in SVO are predictive of norm- violating deviant behavior, and that SVO explains incremental variance in workplace
90 CHAPTER 3 deviance over and above Honesty-Humility, the strongest personality domain predictor of workplace deviance (Lee et al., 2005). Prosocials might have internalized that acting deviantly is a violation of social and organizational norms and therefore largely refrain from such behavior. The threshold to act deviantly then seems to be lower for proselfs because they might not be as concerned with violating equality and fairness norms. Such rule-breaking might be especially prevalent in competitors, individuals who want to harm other involved individuals and who might therefore be even more likely than individualists to act deviantly.5
Possibly, individualists might only act deviantly if it is in their own best interest, whereas competitors might violate organizational norms to harm others. For example, individualists might only come late to work if they have other things to take care of before, whereas competitors might deliberately come late to work to increase their colleagues’ workload.
Future research should examine this further. Importantly, we did not find evidence for our second hypothesis that SVO has a higher predictive validity for interpersonal than for organizational workplace deviance. This suggests that SVO is a powerful predictor irrespectively of the kind of deviant behavior being assessed and that proselfs are just generally more predisposed to act deviantly than prosocials, independently of the dimension of workplace deviance being assessed.
The cross-sectional results for workplace deviance were partly corroborated by using two behavioral measures of deviance: Proselfs disobeyed instructions to a significantly larger extent than prosocials and were more dishonest to increase their gain. This indicates a greater willingness to deviate from norms and rules to increase personal gains, signifying proselfs’ strong focus on themselves and further emphasizing the robustness of the association between
SVO and norm-violating deviant behavior. In fact, individual differences in SVO are generally predictive of norm-violating behaviors, independently of the specific behavior being assessed. The current findings also align with previous research showing that proselfs use strategic misrepresentation and lying more often than prosocials to achieve better outcomes for themselves in interdependent situations (Steinel & De Dreu, 2004). Our results extend this
91 SVO AND DEVIANCE finding by demonstrating that this effect is not only limited to interdependent mixed-motive situations (i.e., to negotiations), but also holds for deviant and dishonest behaviors not directly targeting other individuals. Proselfs are more likely to violate social exchange norms with their coworkers and supervisors, but also with their organization. In addition to this, the current results might suggest that the occurrence of deviant behavior at work can be conceptualized as an interdependent situation between the employee and coworkers or between the employee and the organization. As such, workplace deviance might be closely aligned with defection or noncooperation in social dilemmas. Future research could examine if individuals who defect in social dilemmas are also more likely exhibit norm-violating and deviant behavior in other life situations. Previous research has already found that organizational citizenship behaviors are perceived as social dilemmas in which individual short-term interests conflict with collective long-term interests (Joireman, Kamdar, Daniels,
& Duell, 2006), and the same might apply to deviant behavior at work.
Practical Implications
Overall, individual preferences for equal and fair outcome distributions in interdependent situations play a major role in explaining why individuals behave deviantly.
Individual preferences for cooperation seem to be a strong preventive and protective factor against norm violations and against workplace deviance, and organizations would benefit from hiring individuals who prefer to cooperate with others. Thus, organizations can prevent the occurrence of workplace deviance by selecting job applicants who place higher values on equality and fairness in outcomes (i.e., prosocials). As such, organizations would benefit from acquiring knowledge about applicants’ SVO in selection procedures to screen their proneness to deviant behavior, thereby minimizing the risk of deviant behavior. One way to achieve this would be to directly employ SVO measures in selection settings, which would result in increased efficiency compared to longer, broad personality questionnaires. However, organizations could also try to infer prosocial motives from their applicants through structured
92 CHAPTER 3 interviews, observation techniques, behavior in economic games, or possibly even through
SVO-related judgments by former colleagues or supervisors.
This is especially true given that results of Study 2 demonstrate that SVO explains incremental validity in workplace deviance over and above Honesty-Humility, which is the personality domain that correlates most strongly with workplace deviance (Lee et al., 2005).
Besides the explained incremental validity of SVO over and above Honesty-Humility, the current SVO measures are advantageous compared to regular personality questionnaires regarding the prediction of workplace deviance because they use a (forced) choice methodology and do not rely strongly on the value-laden connotations of language (D. N.
Jackson, Wroblewski, & Ashton, 2000). In other words, SVO measures might be harder to fake in job selection settings than traditional personality questionnaires, such as the Big Five or the HEXACO (Ones & Viswesvaran, 1998).
It has also long been acknowledged that an ethical work climate leads to a reduced occurrence of unethical behavior at work (e.g., Kish-Gephart et al., 2010). In order to activate certain traits (i.e., trait activation theory; Tett & Burnett, 2003), it might be useful to frame work tasks in a way that emphasizes group goals and encourages cooperation between employees and supervisors to prevent the occurrence of workplace deviance. This might potentially amplify the positive effects of prosocial values and negate the negative effects of selfish values on levels of workplace deviance. For example, employees who need to cooperate with one another to attain their goals might show lower levels of workplace deviance.
Limitations and Future Research
This study is not without limitations. First, results on the behavioral measures were slightly equivocal because statistical significance was dependent on the analytical approach of
SVO (continuous or categorical). In addition, the behavioral measures of deviance did not significantly correlate with self-reported workplace deviance (except for interpersonal workplace deviance in Study 2). This could be due to the fact that our measures of behavioral
93 SVO AND DEVIANCE deviance are implicit measures of deviance, which generally do not correlate highly with explicit measures (Hofmann, Gawronski, Gschwendner, Le, & Schmitt, 2005; Nosek, 2007).
However, we believe that the overall results suggest that proselfs are more prone to deviate from norms and rules about destructive deviant behaviors than prosocials. Second and related to this, current SVO measures are relatively abstract and lack the overt significance to signal the usefulness of these tests to applicants. Because applicant reactions to selection tests are important to recruit the best candidates (Smither, Reilly, Millsap, Pearlman, & Stoffey, 1993), it would be valuable to develop a context-specific SVO measure, such as a situational judgment test (McDaniel, Hartman, Whetzel, & Grubb, 2007). Such a contextualized SVO measure would possibly also result in higher validity and better prediction of workplace deviance than the current measures (Morgeson et al., 2007). Third, the effect of SVO on workplace deviance might be different in team settings: Previous research has shown that the presence of a few individuals with rather undesirable traits in teams can lead to desirable outcomes for the entire team (e.g., Narcissism; Goncalo, Flynn, & Kim, 2010). Regarding
SVO, future studies could examine if a certain level of competitive drive (i.e., having one or two proselfs in a team) encourages positive behaviors, whereas a high concentration of proselfs in a team might lead to increased levels of deception and workplace deviance.
Concluding Remarks
The current findings suggest that SVO is not only relevant for predicting behavior in social dilemmas (Balliet et al., 2009), but also in dilemmas that individuals face at work. As such, individual differences in SVO predict norm violations – at work and in situations in which fairness values are pitted against selfish interests. Proselfs indicate higher levels of workplace deviance and are more likely to violate norms to gain (material) benefits than prosocials. SVO, which is a construct deeply rooted in decades of research on game theory and behavior in economic games, has the ability to predict behavior that is essential at the workplace. Organizations could utilize this information to screen job applicants’ SVO as it signals their proneness to deviant behavior in the workplace.
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95 SVO AND DEVIANCE
Footnotes
1 Older studies have often also used the Ring Measure of SVO (Liebrand, 1984;
Liebrand & McClintock, 1988), but this measure shows low test-retest reliability (Murphy et al., 2010).
2 The two-factor structure of workplace deviance also did not fit the data better when only including the 144 participants that were included in the main analysis, CFI = .757; TLI =
.725; RMSEA = .101; SRMR = .095; �2 = 374.73, p < .001, than a one-factor structure of workplace deviance, CFI = .601; TLI = .552; RMSEA = .129; SRMR = .114; �2 = 518.73, p <
.001. The maximum likelihood ratio difference test for �2 between the two estimations was highly significant (�2 difference = 144.00, p < .001), indicating as well that the two-factor solution of workplace deviance fit the data better.
3 The finding that levels of self-reported workplace deviance are generally higher in
Study 2 than in Study 1 could be because participation was completely voluntary in Study 1, whereas participants were compensated for their participation in Study 2. Individuals who participate without compensation in a study are probably more prosocial, which might subsequently have become apparent in the lower levels of self-reported workplace deviance.
4 The results were qualitatively the same when using the regular means: Across three studies, proselfs (n = 225) reported higher levels of organizational (d = 0.447, 95% CI [0.275,
0.619], p < .001), interpersonal (d = 0.416, 95% CI [0.243, 0.589], p < .001), and overall workplace deviance (d = 0.474, 95% CI [0.301, 0.647], p < .001) than prosocials (n = 331).
The effect size was not larger for interpersonal than for organizational workplace deviance,
Q(1) = 0.063, p = .802.
5 Unfortunately, we could not test this because the number of competitors was really low in Study 1 and 2, and Study 3 did not contain any competitors at all. This limited the statistical power to detect a significant effect.
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CHAPTER 4 PERSONALITY AND WORKPLACE DEVIANCE: A META-ANALYSIS
This chapter is based on Pletzer, J. L., Bentvelzen, M., Oostrom, J. K., & De Vries, R. E. (2017). Personality and workplace deviance: A meta-analysis. Manuscript submitted for publication. Paper drafts have been presented at the Dutch-Flemish Research Meeting on Personnel Recruitment and Selection 2017 and at the WAOP 2017 conference.
97 PERSONALITY AND WORKPLACE DEVIANCE
Abstract
We present a comprehensive meta-analysis of the relations between personality and workplace deviance. More specifically, we compared the validities of the Big Five domain scales with the HEXACO domain scales in predicting workplace deviance. By including 68 studies and 460 effect sizes, we found that HEXACO Honesty-Humility shows the strongest relation with workplace deviance, followed by Conscientiousness (Big Five and HEXACO) and Agreeableness (Big Five and HEXACO). Big Five Neuroticism (positively) and
HEXACO Emotionality (negatively) also correlate with workplace deviance. HEXACO and
Big Five Openness to Experience and Extraversion show either non-significant or negligible correlations with workplace deviance. For the most part, these results support the conceptual differences between the Big Five and the HEXACO personality models. Importantly, none of the personality domain scales (Big Five and HEXACO) correlate differently with the two facets of workplace deviance (i.e., interpersonal and organizational workplace deviance).
Based on a meta-analytic structural equation modeling analysis, we found that the HEXACO domain scales (24.9%) explain more variance in workplace deviance than the Big Five domain scales (17.1%). Consequently, the HEXACO model appears to be a viable alternative to the Big Five model when predicting and explaining levels of workplace deviance.
Theoretical and practical implications of the findings as well as limitations and future research ideas are discussed.
Keywords: counterproductive work behavior, workplace deviance, personality, Big
Five, HEXACO
98 CHAPTER 4
Introduction
Workplace deviance poses a serious and pervasive problem for organizations because of its substantial negative impact; for example, it decreases task performance (Sackett, 2002), impairs team performance (Dunlop & Lee, 2004), and leads to increased stress levels among coworkers (Cortina, Magley, Williams, & Langhout, 2001). Accordingly, the financial costs of workplace deviance are estimated to be very high (Henle et al., 2005; Robinson & Bennett,
1995), but might not even capture the true extent because of the hidden nature of such behaviors. Because of the high costs associated with workplace deviance, the prevention and prediction of workplace deviance has been a major focus in science and practice. One commonly used predictor of workplace deviance is personality, which is usually captured with the Big Five (B5) or the Five-Factor Model of personality (FFM) (e.g., Berry, Ones, &
Sackett, 2007; Salgado, 2002). Despite important advances due to previous meta-analyses examining the relations between personality and workplace deviance (Berry, Carpenter, &
Barratt, 2012; Berry et al., 2007; Salgado, 2002), much is to be gained from a meta-analysis strictly focusing on personality as a predictor of workplace deviance, especially because many unresolved issues remain in examining these relations.
First, previous meta-analyses (Berry et al., 2012; Salgado, 2002) include only a limited number of effect sizes and even found substantially different effect sizes for some of the B5 or FFM personality domain scales. For example, Salgado (2002) found only a small correlation for Conscientiousness (r = -.16)1, whereas Berry and colleagues (2012) report a moderate correlation for Conscientiousness with self-reported workplace deviance (r = -.31; data from Berry et al., 2007). This creates ambiguity about which personality domain scales are most useful in predicting workplace deviance. Second, whereas the B5 and FFM have been the dominating model of personality for the past decades, considerable evidence has accumulated in favor of an alternative representation of personality structure in recent years, known as the HEXACO-model (e.g., Ashton, Lee, & De Vries, 2014; Lee & Ashton, 2004).
The HEXACO-model consists of rotated variants of the ‘Big Five’ Neuroticism2 and
99 PERSONALITY AND WORKPLACE DEVIANCE
Agreeableness domain scales, but also includes a sixth domain scale named ‘Honesty-
Humility’ (Lee & Ashton, 2004). Although some primary studies have used the HEXACO personality domain scales in predicting workplace deviance (e.g., Chirumbolo, 2015; Louw,
Dunlop, Yeo, & Griffin, 2016), the HEXACO personality domain scales have not been included in any of the previous workplace deviance meta-analyses. And third, the small number of effect sizes in previous meta-analyses did not allow for testing important moderators of the relations between personality and workplace deviance. The goal of the present meta-analysis therefore is to examine and compare the effect sizes of the B5/FFM personality domain scales and the HEXACO personality domain scales, and to test the moderating effects of several demographic and methodological characteristics on the relations between personality and workplace deviance.
Workplace Deviance
Workplace deviance (or counterproductive work behavior) has been defined as
“voluntary behavior that violates significant organizational norms and, in so doing, threatens the well-being of the organization or its members, or both” (Robinson & Bennett, 1995, p.
556). Such behavior has severe negative effects on the well-being and success of organizations and their employees (e.g., Barling, Dupré, & Kelloway, 2009; Bowling, Burns,
Stewart, & Gruys, 2011). Workplace deviance is often divided into two facets: Organizational workplace deviance (OD) and interpersonal workplace deviance (ID) (Bennett & Robinson,
2000). OD consists of behaviors directed toward the organization, such as stealing, damaging company property, or intentionally working slowly. ID consists of behaviors directed toward members of the organization, such as gossiping, bullying, or harassing coworkers. Both forms are costly and detrimental for the organization and can vary in severity (Henle et al., 2005;
Sackett, 2002). The prediction and prevention of workplace deviance is a major focus for scientists and practitioners, especially in job selection settings (Ones, Dilchert, Viswesvaran,
& Judge, 2007). Deviant workplace behavior can be caused by the organizational environment (e.g., because of abusive supervision; Mitchell & Ambrose, 2007) and by stable
100 CHAPTER 4 individual differences (e.g., personality; Hastings & O’Neill, 2009). Although various individual differences have been examined as predictors of workplace deviance (e.g., age, gender, work experience), personality might be the most prominent predictor of workplace deviance (e.g., Berry et al., 2012; Ng, Lam, & Feldman, 2016). As such, personality questionnaires are a useful tool in job selection settings to predict an applicant’s future job performance and to screen an applicant’s proneness to workplace deviance (e.g., Ones et al.,
2007).
Personality
Personality describes “the set of psychological traits and mechanisms within the individual that are organized and relatively enduring and that influence his or her interactions with, and adaptations to, the intrapsychic, physical, and social environments” (Larsen & Buss,
2005, p. 4). The most common approach to study the structure of personality is through the so-called lexical approach, which posits that important human personality differences are encoded in sufficiently encompassing dictionaries in all natural languages (Goldberg, 1982;
Goldberg, 1990). Up until recently, consensus existed among personality scholars that five domain scales capture most of the personality variance. This model of personality is referred to as the B5 (Goldberg, 1990) or the FFM (McCrae & Costa, 1992). Because of their different approach to study personality – the B5 is based on the lexical approach to personality, whereas the FFM is based on a factor analytic examination of personality using the NEO
Personality Inventory (McCrae & Costa, 1992) – some differences exist between the B5 and the FFM about how to best name and interpret the personality domain scales, and about which facets belong to which personality domain scale. Nevertheless, most scholars agree that they are overall highly similar (Ashton & Lee, 2005). In this meta-analysis, we will therefore treat the B5 and the FFM interchangeably to represent research on personality that assumes that personality is best represented using five separate domains (from here on referred to as B5), but will investigate if the effect sizes with workplace deviance differ between these two personality models. The B5 divides personality into the following five domain scales:
101 PERSONALITY AND WORKPLACE DEVIANCE
Openness to Experience, Conscientiousness, Extraversion, Agreeableness, and Neuroticism
(see Table 1 for a description of characteristics associated with each B5 personality domain scale).
Table 1 Big Five Personality Domain Scales and their Associated Characteristics B5 domain scale Characteristics Openness Intellectual Curiosity, Aesthetic Sensitivity, Creative Imagination Conscientiousness Organization, Productiveness, Responsibility Extraversion Sociability, Assertiveness, Energy Level Agreeableness Compassion, Respectfulness, Trust Neuroticism Anxiety, Depression, Emotional Volatility Note. Characteristics are from Soto and John (2016). These differ slightly between questionnaires that are based on the B5 and those that are based on the FFM.
Although the B5 is the predominant model of personality, re-analyses of lexical data that have become available from at least a dozen languages, including English, offer support for six cross-culturally replicable factors of personality (Ashton et al., 2014; De Raad et al.,
2014; Saucier, 2009), which are commonly known by the HEXACO acronym: Honesty-
Humility, Emotionality, eXtraversion, Agreeableness, Conscientiousness, and Openness to experience (see Table 2 for a description of characteristics associated with each HEXACO personality domain scale). The HEXACO domain scales Extraversion, Conscientiousness, and Openness to Experience are highly similar to their B5 counterparts. The other three domain scales – Honesty-Humility, Emotionality, and Agreeableness – differ in important ways from the Neuroticism (versus Emotional Stability) and Agreeableness domain scales of the B5 (Ashton & Lee, 2008).
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Table 2 HEXACO Personality Domain Scales and their Associated Characteristics HEXACO domain scales Characteristics Honesty-Humility Sincerity, Fairness, Greed Avoidance, Modesty Emotionality Fearfulness, Anxiety, Dependence, Sentimentality Extraversion Social Self-Esteem, Social Boldness, Sociability, Liveliness Agreeableness Forgiveness, Gentleness, Flexibility, Patience Conscientiousness Organization, Diligence, Perfectionism, Prudence Openness to Experience Aesthetic Appreciation, Inquisitiveness, Creativity, Unconventionality Note. Descriptions of the characteristics can be found at hexaco.org/scaledescriptions.
More specifically, HEXACO Emotionality and Agreeableness are rotated variants of
B5 Neuroticism and Agreeableness. High levels of HEXACO Emotionality are associated with higher levels of B5 Neuroticism and somewhat higher levels of B5 Agreeableness, and high levels of HEXACO Agreeableness are associated with higher levels of B5 Agreeableness and somewhat lower levels of B5 Neuroticism. This re-rotation is accompanied by a shift in the content of these domains. For example, the irritability and anger content that is an element of B5 Neuroticism is part of Agreeableness in the HEXACO model. On the other hand, B5
Agreeableness captures some of the sentimentality content that is part of the HEXACO
Emotionality factor. Furthermore, especially in the FFM, Agreeableness has been found to capture parts of the sixth HEXACO domain scale Honesty-Humility (Ashton & Lee, 2005); however, this is somewhat less the case for some Big Five questionnaires, such as the Big
Five Inventory (e.g., BFI-2; Soto & John, 2017). Hence, although some of the B5 and
HEXACO counterparts have similar sounding names, such as Agreeableness and
Emotionality/Emotional Stability, there are conceptual differences that may influence their relations with criterion variables, such as workplace deviance. Furthermore, Honesty-
Humility reflects the tendency to be fair and genuine in dealing with others (Ashton & Lee,
2007), and low levels of Honesty-Humility are associated with harmful effects upon individuals and upon society and humanity as a whole, such as theft, fraud, workplace delinquency, and vandalism (Ashton & Lee, 2008). It has been suggested that this factor, representing individual differences in reluctance versus willingness to exploit others, is
103 PERSONALITY AND WORKPLACE DEVIANCE especially important in predicting workplace deviance, yet it is not sufficiently captured by any of the B5 domain scales (Ashton et al., 2014; Ashton, Lee, & Son, 2000).
Personality and Workplace Deviance
Given that the B5 and HEXACO personality domain scales of Extraversion,
Conscientiousness, and Openness to Experience are conceptually very similar, we expect similar relations for these three personality domain scales with the criterion variable workplace deviance. In line with previous meta-analytic results, we do not expect Openness to Experience (Salgado, 20023: r = .10; Berry et al., 20074: r = -.06) and Extraversion
(Salgado, 2002: r = .01; Berry et al., 2007: r = -.03) to relate to workplace deviance.
However, because individuals scoring high on Conscientiousness are hard-working, disciplined, and responsible, and because previous meta-analytic results indicated a negative relation between Conscientiousness and workplace deviance (Salgado, 2002: r = -.16; Berry et al., 2007: r = -.31), we expect Conscientiousness to negatively relate to workplace deviance.
Agreeableness is expected to relate negatively to workplace deviance because individuals scoring high on this domain scale are compassionate, respectful, and trusting.
Meta-analytic evidence also indicates that Agreeableness correlates negatively with workplace deviance (Salgado, 2002: r = -.13; Berry et al., 2007: r = -.35). As noted above, B5
(and especially FFM) Agreeableness captures some of the variance associated with HEXACO
Honesty-Humility, which has been found to be an important predictor of workplace deviance
(Lee, Ashton, & De Vries, 2005). However, compared to HEXACO Agreeableness, B5
Agreeableness lacks a (reversed) anger facet which is part of B5 Neuroticism and which has been shown to correlate positively with workplace deviance (Hastings & O’Neill, 2009).
Consequently, through its inclusion of Honesty-Humility variance and through its exclusion of (reversed) anger-related variance, B5 Agreeableness might be either somewhat more or somewhat less strongly negatively related to workplace deviance than HEXACO
Agreeableness. Previous findings from primary studies are mixed as well (e.g., Lee, Ashton,
104 CHAPTER 4
& De Vries, 2005), rendering a meta-analytic examination of this relation even more important.
As noted above, B5 Neuroticism contains variance associated with anxiety and depression and variance associated with irritability and anger. Anxiety may be associated with lower levels of workplace deviance, whereas anger may be associated with higher levels of workplace deviance. Previous meta-analyses remain ambiguous about the relation between
Neuroticism and workplace deviance as well, reporting either a non-significant (Salgado,
2002: r = .04) or a positive relation between B5 Neuroticism and workplace deviance (Berry et al., 2007: r = .23, note that the original correlation of Berry et al. is negative because these authors used B5 Emotional Stability instead of Neuroticism). A new meta-analysis on this relation is therefore necessary to determine whether Neuroticism relates positively or negatively to workplace deviance. Individuals scoring high on HEXACO Emotionality combine higher fearfulness and anxiety with a higher need for emotional support and a tendency to form strong bonds with others, which would lead us to expect that high levels of
HEXACO Emotionality are associated with lower levels of workplace deviance. This aligns with the finding that individuals scoring high on HEXACO Emotionality are less likely to be deviant because they are more likely to be afraid of retributions (Van Gelder & De Vries,
2012). Thus, we expect HEXACO Emotionality to be negatively related to workplace deviance, although such a relation is not likely to be very strong. Last, we would expect
HEXACO Honesty-Humility to show the strongest negative correlation with workplace deviance out of all included personality domain scales because individuals scoring high on this trait tend to be honest, fair-minded, and tend to lack greed. These individuals have also been found to be more cooperative (Thielmann & Hilbig, 2014), less likely to sexually harass someone (Lee et al., 2003), and less likely to be delinquent and criminal (De Vries & Van
Gelder, 2013, 2015).
As described above, two facets of workplace deviance have been distinguished: ID and OD. Whether ID and OD are two separate facets of an overall workplace deviance
105 PERSONALITY AND WORKPLACE DEVIANCE construct is debated in the literature. Meta-analytic evidence indicates that these two domain scales correlate strongly, but not too strongly with each other (r = .52) and that they show different correlations with some personality domain scales and with OCB (Berry et al., 2007).
Agreeableness correlates more strongly with ID (r = -.36, k = 10) than with OD (r = -.25, k =
8), whereas Conscientiousness correlates more strongly with OD (r = -.34, k = 8) than with ID
(r = -.19, k = 11) (Berry et al., 2007). The other three personality domain scales either did not correlate strongly with workplace deviance (i.e., Extraversion) or did not differ significantly in their relations with ID or OD (i.e., Openness to Experience and Neuroticism).5 However, factor analytic evidence about the separability of ID and OD is inconclusive, with at least one study failing to replicate the two-factor structure of workplace deviance (Lee & Allen, 2002).
In the current meta-analysis, we will examine if ID and OD correlate differently with personality domain scales based on a larger number of studies. Results will provide further evidence for the usefulness of separating ID and OD when personality is the predictor.
Moderating Variables
Much of the variability in findings between previous meta-analyses and between primary studies might be explained by differences in demographic or methodological characteristics of the included studies that moderate the relations between personality and workplace deviance. Meta-analytic evidence indicates that women and older employees are slightly less likely to be deviant (Berry et al., 2007; Ng et al., 2016), and also indicates gender differences in personality (Feingold, 1994; see also De Vries, Ashton, & Lee, 2009, for gender differences in Honesty-Humility). Hence, the average percentage of women and the average age of participants in the included studies was included as an exploratory moderator of the relations between personality and workplace deviance. Results might have implications for studies of the personality-workplace deviance relations in which samples are age and gender diverse. In addition, we examine if the questionnaire used to assess workplace deviance (following Berry et al., 2007, we will compare Bennett & Robinson's, 2000, questionnaire versus other questionnaires) and the source of the workplace deviance rating
106 CHAPTER 4
(self- versus other-rated) function as moderators. This may have important methodological implications for future studies that examine the relations between personality and workplace deviance. For example, it might be that the Bennett and Robinson (2000) questionnaire captures deviance domains that are more strongly related to personality than other questionnaires. In addition, stronger correlations of personality with self-reported workplace deviance may be indicative of same-source biases. Last of all, we examine if the questionnaire used to assess the B5 (based on either Goldberg's (1990) Big-Five model or on McCrae and
Costa's, 1992, Five-Factor model) and the number of personality questionnaire items influence the magnitude of effect sizes. Results of these moderator analyses may indicate whether the B5 and FFM inventories can be used interchangeably when predicting workplace deviance and may provide evidence as to whether the number of items used to assess personality domain scales is important when examining the personality-workplace deviance relations. It is expected that longer questionnaires contain more reliable domain scales, resulting in increased levels of validity.
Contributions of the Current Meta-Analysis
The current meta-analysis adds to the existing literature in the following ways. First, we provide the first comprehensive overview of the relations between personality and workplace deviance for both the B5 model and the HEXACO model. Second, we extend previous meta-analyses that have examined the relations between personality and workplace deviance (Berry et al., 2012, 2007; Salgado, 2002). Third, we examine the relations between personality and ID and OD based on a large number of included effect sizes. Fourth, we examine important methodological moderators of the relations of interest (e.g., the source of the workplace deviance rating). Fifth and last, we compare the effect sizes between the B5 and HEXACO personality domain scales and, most importantly, we examine whether the B5 or the HEXACO explains more variance in workplace deviance.
Method
Systematic Literature Search
107 PERSONALITY AND WORKPLACE DEVIANCE
A systematic literature search was conducted in several scientific databases, including
EBSCO, Web of Science, and Google Scholar. The keywords used to find articles were:
Personality, Big 5, Big Five, Five-factor-model, FFM, HEXACO, Agreeableness,
Extraversion, Openness to Experience, Neuroticism, Emotional Stability, Emotionality,
Conscientiousness, Intellect, Honesty-Humility, Workplace Deviance, Interpersonal
Deviance, Organizational Deviance, or Counterproductive Work Behavior. The keywords had to be mentioned in the abstract or title of the study. After removing duplicates, 739 scientific articles were identified. By examining previous meta-analyses on personality or workplace deviance (Berry et al., 2012, 2007; Dalal, 2005; Grijalva & Newman, 2014; Salgado, 2002;
Spector, 2011; Woo, Chernyshenko, Stark, & Conz, 2014), six additional scientific articles were found. In addition, some authors were contacted for more data or articles on the topic, which resulted in four additional articles. Thus, the final number of scientific articles was 749.
All articles were fully examined.
For the inclusion or exclusion of studies in this meta-analysis, several criteria had to be met. First, the correlation coefficient (r) between workplace deviance and at least one domain scale of personality had to be reported, along with the sample size. Second, the personality measure used in the study had to be based either on the B5/FFM model or the
HEXACO model. Third, all studies had to be field studies to be included. Experimental studies were not included in this meta-analysis. Fourth, workplace deviance had to be measured on an individual and not on a group level. We also excluded one study (Spector &
Zhou, 2014) because there seemed to be some overlap in data with another study included in this meta-analysis (Zhou, Meier, & Spector, 2014). The inclusion criteria resulted in a final sample of 68 individual studies and 460 effect sizes in the overall analysis. The articles were published between 1998 and 2016, with a median publication year of 2011. All effect sizes and study characteristics were independently coded by the first and second author. The agreement among the independent raters was 98%. All inconsistencies in the codings were
108 CHAPTER 4 resolved after discussion. The codings for each included effect size and their references are listed in Table 3 (for B5) and in Table 4 (for HEXACO).
Definition of Variables
Big Five model. The B5 model measures five personality domain scales: Openness to
Experience (k = 28), Conscientiousness (k = 54), Extraversion (k = 29), Agreeableness (k =
46), and Neuroticism (k = 41).
HEXACO model. The HEXACO model measures six personality domain scales:
Honesty-Humility (k = 16), Emotionality (k = 13), Extraversion (k = 13), Agreeableness (k =
13), Conscientiousness (k = 14), and Openness to Experience (k = 13).
Workplace deviance. Workplace deviance can be measured as an overall construct or divided into two separate constructs, OD and ID (Bennett & Robinson, 2000). OD includes all deviant behaviors directed at the organization in which an individual is employed (k for B5 domain scales = 15 – 33; k for HEXACO domain scales = 3 – 5). ID includes all deviant behaviors directed at individuals in the organization (k for B5 domain scales = 16 – 30; k for
HEXACO domain scales = 2 – 4). Overall workplace deviance describes the combination of these two types. Studies that assessed only one specific form of deviant workplace behavior, such as stealing, were not included in this study.
109 PERSONALITY AND WORKPLACE DEVIANCE
Table 3 Studies, Effect Sizes, and Codings included in the B5 – WD Meta-Analyses WD r # WD WD % Study N B5 Q Age form O C E A N items Q rater Women Other (C); Alias et al. (2013) OD --- -.38 --- -.40 --- 429 12 B&R SR --- 64.6 FFM (A) Other (C); ID --- -.13 --- -.35 --- 429 12 B&R SR --- 64.6 FFM (A) Ashton (1998) WD -.01 -.22 .09 -.21 -.04 131 BFI 20 --- SR --- 60.0 Avey et al. (2010) WD --- -.38 -.28 ------336 BFI 10 O SR 32.0 --- Bernerth et al. OD -.04 -.11 .00 -.02 -.08 113 BFI 8 O OR 37.8 39.0 (2012) ID -.08 .01 -.02 -.15 .12 113 BFI 8 O OR 37.8 39.0 Bollmann & Krings OD --- -.31 --- -.24 .10 158 Other 6 O SR --- 53.8 (2016) ID --- -.08 --- -.30 -.07 158 Other 6 O SR --- 53.8 *Bolton et al. OD .02 -.31 -.18 -.17 .23 233 BFI --- O SR 38.6 --- (2010) ID .02 -.18 -.04 -.32 .23 233 BFI --- O SR 38.6 --- WD .02 -.28 -.14 -.28 .27 233 BFI --- O SR 38.6 --- Bowling (2010) WD --- -.35 ------209 BFI 10 B&R SR 33.0 56.0 Bowling & ID --- -.33 --- -.38 --- 726 BFI 10 B&R SR 38.0 55.0 Eschleman (2010) OD --- -.38 --- -.35 --- 727 BFI 10 B&R SR 38.0 55.0 Bowling et al. OD --- -.35 ------227 BFI 10 B&R SR 38.7 59.0 (2010) Bowling et al. ID --- -.37 --- -.48 --- 193 BFI 10 B&R SR 20.1 64.0 (2011) S1 OD --- -.36 --- -.34 --- 193 BFI 10 B&R SR 20.1 64.0 Bowling et al. ID --- -.28 --- -.32 --- 220 BFI 10 B&R SR 39.8 57.0 (2011) S2 OD --- -.33 --- -.30 --- 220 BFI 10 B&R SR 39.8 57.0 *Chang & WD -.29 -.47 -.36 -.41 .21 1662 FFM 12 O SR 31.2 68.0 Smithikrai (2010) Colbert et al. (2004) ID ------.50 --- 173 BFI 10 B&R OR 32.6 48.0 S3 Colbert et al. (2004) ID ------.55 --- 122 BFI 10 B&R OR 33.1 68.0 S4 Coyne et al. (2013) OD .06 -.30 -.12 -.19 .45 105 BFI 10 O SR 31.9 44.0 S1 ID .06 -.13 .11 -.37 .21 105 BFI 10 O SR 31.9 44.0 Coyne et al. (2013) OD -.05 -.28 -.02 -.08 .19 203 BFI 10 O SR 33.9 28.0 S2 ID -.04 -.18 -.03 -.12 .11 203 BFI 10 O SR 33.9 28.0 Coyne et al. (2013) OD -.09 -.29 .03 -.22 .16 185 BFI 10 O SR 29.0 33.0 S3 ID -.01 -.30 -.03 -.25 .12 185 BFI 10 O SR 29.0 33.0 Coyne et al. (2013) OD .06 -.37 .22 -.07 .10 70 BFI 10 O SR 35.6 54.0 S4 ID -.04 -.02 .02 -.19 .19 70 BFI 10 O SR 35.6 54.0 Ferris et al. (2009) OD --- -.25 --- -.27 .08 230 BFI 8-9 O SR 42.5 47.0 *Flaherty & Moss WD -.13 -.28 -.18 -.18 .40 131 FFM 12 O SR 44.7 64.9 (2007) Flaherty & Moss WD .01 .01 -.11 -.06 .09 131 FFM 12 O OR 44.7 64.9 (2007) Guay et al. (2016) OD -.01 -.34 -.05 -.12 .00 113 BFI 10 B&R OR 32.6 41.6 ID .02 -.20 -.06 -.18 .06 113 BFI 10 B&R OR 32.6 41.6 Hastings & O’Neill WD -.20 -.39 -.06 -.47 .12 198 BFI 24 B&R SR 18.9 67.5 (2009) Hitlan & Noel ID -.19 -.01 .14 -.28 -.03 104 FFM 12 O SR 43.2 36.2 (2009) OD -.02 -.41 -.25 -.33 .31 104 FFM 12 O SR 43.2 36.2 Jensen & Patel OD -.09 -.12 -.09 -.31 .22 517 BFI 10 O SR 33.5 53.0 (2011) ID -.07 -.41 -.08 -.23 .21 517 BFI 10 O SR 33.5 53.0
110 CHAPTER 4
Kluemper et al. ID .22 -.08 .02 .05 .13 220 BFI 30 B&R SR 22.7 55.0 (2013) S1 OD .11 -.04 .09 .05 -.10 220 BFI 30 B&R SR 22.7 55.0 Kluemper et al. ID .02 -.29 .30 .27 -.19 100 FFM 12 O OB 25.8 49.0 (2013) S2 *Kluemper et al. WD -.03 -.27 -.03 -.23 .19 233 BFI 10 O OR 31.1 60.0 (2014) S1 *Kluemper et al. WD -.01 -.30 .07 -.39 .19 230 BFI 10 B&R SR 37.0 57.0 (2014) S2 Kluemper et al. WD -.12 -.27 -.08 -.32 .24 224 BFI 10 B&R OR 37.0 57.0 (2014) S2 Le et al. (2011) S1 WD --- -.23 ------.25 569 Other 11-14 --- OR 46.3 56.0 Le et al. (2011) S2 WD --- -.10 ------.04 925 Other 11-14 O OR 41.2 66.3 *Lee, Ashton, & De WD .12 -.27 .09 -.29 .04 106 BFI 10 O SR 26.4 45.3 Vries (2005) S1 *Lee, Ashton, & De WD .20 -.28 .13 -.05 .10 128 BFI 10 O SR 21.0 64.1 Vries (2005) S2 *Lee, Ashton, & De WD -.01 -.41 .12 -.26 -.06 179 FFM 12 O SR 20.7 55.9 Vries (2005) S3 Lee, Ashton, & ID -.08 -.21 .23 -.12 -.30 267 Other --- B&R SR 37.6 50.0 Shin (2005) OD -.06 -.24 .18 -.07 -.02 267 Other --- B&R SR 37.6 50.0 Liao et al. (2004) OD -.12 -.38 -.05 -.30 .20 286 BFI 10 B&R SR 26.4 67.0 ID .02 -.38 .06 -.40 .17 286 BFI 10 B&R SR 26.4 67.0 Meyer et al. (2014) WD --- -.47 --- -.47 --- 588 BFI --- B&R SR 39.1 47.0 Miller (2015) OD --- -.39 ------428 BFI 10 O SR 22.2 40.0 Morris et al. (2015) ID --- -.31 --- -.42 .13 285 FFM 48-56 B&R SR 19.4 68.5 OD --- -.36 --- -.38 .16 285 FFM 48-56 B&R SR 19.4 68.5 WD --- -.34 --- -.41 .14 285 FFM 48-56 B&R SR 19.4 68.5 Mount et al. (2006) OD -.25 -.44 -.12 -.41 .47 141 FFM 20-30 B&R SR 32.0 65.0 ID -.30 -.16 .05 -.43 .24 141 FFM 20-30 B&R SR 32.0 65.0 OD -.23 -.22 -.16 -.05 .21 141 FFM 20-30 B&R OR 32.0 65.0 ID -.17 -.19 -.03 -.21 .18 141 FFM 20-30 B&R OR 32.0 65.0 O'Brien & Allen ID --- -.14 ------207 BFI 10 B&R OR 21.5 75.0 (2007) OD --- -.15 ------207 BFI 10 B&R OR 21.5 75.0 ID --- -.26 ------207 BFI 10 B&R SR 21.5 75.0 OD --- -.45 ------207 BFI 10 B&R SR 21.5 75.0 O’Neill & Hastings ID -.06 -.14 -.14 -.27 .06 149 BFI 10 B&R SR 18.3 72.8 (2011) OD -.07 -.42 -.05 -.24 .14 149 BFI 10 B&R SR 18.3 72.8 WD -.07 -.42 -.05 -.28 .12 149 BFI 10 B&R SR 18.3 72.8 Other O’Neill et al. (2011) WD --- -.47 --- -.33 .26 464 (CA); BFI --- B&R SR ------(N) Other ID --- -.41 --- -.33 .22 464 (CA); BFI --- B&R SR ------(N) Other OD --- -.48 --- -.26 .26 464 (CA); BFI --- B&R SR ------(N) Oh et al. (2014) S1 ID --- .03 --- -.18 .18 144 BFI 10 B&R SR 21.1 47.0 OD --- -.24 --- -.16 .14 144 BFI 10 B&R SR 21.1 47.0 Oh et al. (2014) S2 ID --- .03 --- -.17 .19 108 BFI 10 B&R OR 27.6 40.0 OD --- -.24 --- -.07 .04 108 BFI 10 B&R OR 27.6 40.0 Peng (2012) S1 ID --- -.26 ------161 Other --- B&R SR 34.2 37.0 OD --- -.41 ------161 Other --- B&R SR 34.2 37.0 *Peng (2012) S2 WD -.25 -.54 -.20 -.34 .21 366 BFI 2 O SR --- 38.0 Penney et al. (2011) WD --- -.09 ------.22 239 BFI 10 O SR 41.1 56.0 *Richards & Schat WD -.35 -.19 -.06 -.28 .17 146 BFI 10 B&R OR 37.0 50.0 (2011) *Sackett et al. ID -.07 -.30 -.02 -.40 .37 900 BFI 10 B&R SR 43.4 76.0 (2006)
111 PERSONALITY AND WORKPLACE DEVIANCE
OD -.03 -.54 -.13 -.28 .30 900 BFI 10 B&R SR 43.4 76.0 WD -.08 -.52 -.13 -.38 -.39 900 BFI 10 B&R SR 43.4 76.0 *Scherer et al. WD .06 -.15 -.04 -.17 .24 193 BFI 10 O SR 24.0 73.0 (2013) Shoss et al. (2016) OD --- -.22 --- -.12 .22 461 BFI 10 O SR 44.8 50.0 ID --- -.16 --- -.17 .23 461 BFI 10 O SR 44.8 50.0 Smithikrai (2008) WD --- -.49 --- -.42 --- 612 FFM 12 O SR 31.1 68.0 Spector & Che WD --- -.14 ------.21 146 BFI 10 O SR 22.1 75.0 (2014) WD --- -.22 ------.13 146 BFI 10 O OR 22.1 75.0 Sulea et al. (2013) OD --- -.23 --- -.14 .15 236 Other 10-19 O SR 38.1 54.0 Yang & Diefendorff OD --- -.15 --- -.19 --- 231 FFM 12 O SR 27.8 70.0 (2009) ID --- -.20 --- -.24 --- 231 FFM 12 O SR 27.8 70.0 Zhou et al. (2014) OD --- -.37 --- -.32 .21 932 BFI 10 O SR 21.8 78.0 ID --- -.28 --- -.41 .15 932 BFI 10 O SR 21.8 78.0 Note. ID = Interpersonal workplace deviance, OD = Organizational workplace deviance, WD = overall workplace deviance; O = Openness, C = Conscientiousness, E = Extraversion, A = Agreeableness, N = Neuroticism; B5 Q = Big Five personality questionnaire; BFI = personality questionnaire based on the Big Five Inventory (i.e., Goldberg, 1990); FFM = personality questionnaire based on the Five-Factor Model (McCrae & Costa, 1992); # items = the number of items used in each study to assess one personality domain scale (a few studies used different numbers of items for different personality domain scales; we then included the range of the number of items used in the respective study in the table); WD Q = questionnaire used to assess workplace deviance; B&R = Bennett and Robinson’s (2000) workplace deviance measure; O = Other measure used to assess personality or workplace deviance; WD rater = source of the workplace deviance rating; SR = self-rated workplace deviance, OR = other-rated workplace deviance, OB = WD rated by objective company records; Age = the average age of participants in each study; % women = the average percentage of women in each study. *Included in the two-stage meta-analytical structural equation model.
112 CHAPTER 4
Table 4 Studies, Effect Sizes, and Codings included in the HEXACO – WD Meta-Analyses r WD # WD WD % Study N Age form items Q rater Women H E X A C O *Chirumbolo WD -.20 .09 .01 -.07 -.06 -.21 203 10 O SR 41.1 53.7 (2015) *De Vries (2014) WD -.47 -.12 .01 -.22 -.37 .13 238 32 O SR 32.9 47.9 De Vries & Van WD -.34 -.05 .01 -.15 -.22 .09 455 32 O SR 45.6 45.3 Gelder (2015) De Vries et al. WD -.30 ------.32 --- 289 24-32 O SR 37.9 77.9 (2014) *Lee, Ashton, & De WD -.51 -.29 .10 -.24 -.16 -.06 106 18 O SR 26.4 45.3 Vries (2005) S1 *Lee, Ashton, & De WD -.34 .01 .09 -.14 -.34 .18 128 18 O SR 21.0 64.1 Vries (2005) S2 *Lee, Ashton, & De WD -.55 -.28 .15 -.25 -.38 .07 179 18 O SR 20.7 55.9 Vries (2005) S3 Lee, Ashton, & ID -.25 ------276 --- B&R SR 37.6 50.0 Shin (2005) OD -.33 ------276 --- B&R SR 37.6 50.0
Louw et al. (2016) OD -.46 -.20 .00 -.09 -.43 .07 114 16 B&R SR 30.4 52.6 *Marcus et al. WD -.46 -.23 .07 -.03 -.31 -.02 169 16 O SR 21.5 74.0 (2007) S1 *Marcus et al. WD -.38 -.13 .06 -.10 -.35 .01 496 16 O SR --- 59.0 (2007) S2 O’Neill et al. (2011) WD -.36 ------464 16 B&R SR ------
ID -.32 ------464 16 B&R SR ------
OD -.33 ------464 16 B&R SR ------*Pletzer et al. ID -.54 -.16 -.08 -.21 -.55 -.38 337 16 B&R SR 34.5 30.0 (2015) S1 OD -.48 -.13 -.13 -.14 -.56 -.31 337 16 B&R SR 34.5 30.0
WD -.52 -.15 -.11 -.17 -.58 -.35 337 16 B&R SR 34.5 30.0 *Pletzer et al. ID -.43 .01 -.13 -.19 -.42 -.31 441 16 B&R SR 33.2 24.0 (2015) S2 OD -.47 .03 -.23 -.17 -.18 -.31 441 16 B&R SR 33.2 24.0
WD -.47 -.03 -.20 -.19 -.47 -.32 441 16 B&R SR 33.2 24.0 *Wiltshire et al. WD -.47 .05 -.29 -.19 -.58 -.26 268 10 B&R SR 40.3 51.0 (2014) *Zettler & Hilbig WD -.37 -.20 -.13 -.13 -.37 -.06 148 --- B&R SR 35.0 48.0 (2010) Note. ID = Interpersonal workplace deviance, OD = Organizational workplace deviance, WD = overall workplace deviance; H = Honesty-Humility, E = Emotionality, X = Extraversion, A = Agreeableness, C = Conscientiousness, O = Openness to Experience; # items = the number of items used in each study to assess one personality domain scale; WD Q = questionnaire used to assess workplace deviance; B&R = Bennett and Robinson’s (2000) workplace deviance measure; O = other measure used to assess workplace deviance; WD rater = source of the workplace deviance rating; SR = self-rated workplace deviance, OR = other-rated workplace deviance; Age = the average age of participants in each study; % women = the average percentage of women in each study; *Included in the two-stage meta-analytical structural equation model.
113 PERSONALITY AND WORKPLACE DEVIANCE
Moderator Variables
Percentage of women. Except for three individual studies, all studies mentioned the percentage of women. Across all included studies, the percentage of women ranged from 24% to 78% and the average percentage of women among all studies was 55.3%.
Age. Across the individual studies that mentioned the average age of the sample (k =
62), it ranged between 18.3 to 46.3 years, with an average of 32.1 years.
Personality measure. The B5 domain scales were assessed with a variety of questionnaires. Within this meta-analysis, 57 of the included individual studies used a B5 measure. Most of the studies used a questionnaire based on Goldberg (1982; 1990; B5; k per domain scales = 21 – 39), followed by questionnaires based on Costa and McCrae (1992;
FFM; k per domain scales = 6 – 9). The remaining studies used other questionnaires or a combination of questionnaires (e.g., Dawson, 1996; Johnson, 2002; Ostendorf, 1990; Sava,
2008). All included studies that examined the HEXACO domain scales used the HEXACO personality inventory (Lee & Ashton, 2004).
Number of items. The different personality questionnaires varied in the number of items they contained. For each personality domain scale, the number of items was coded. For the B5 domain scales, this ranged from 2 to 56, with a median of 10 items. For the HEXACO domain scales, this ranged from 10 to 32 with a median of 16 items (see Tables 3 and 4 for the codings).
Workplace deviance questionnaire. Workplace deviance can be assessed with a variety of different questionnaires. Following Berry et al. (2007), we tested if the relations between personality and workplace deviance differed depending on whether Bennett and
Robinson's (2000; k for B5 domain scales = 11 – 25; k for HEXACO domain scales = 5 – 7) questionnaire or another questionnaire or combination of those (i.e., Aquino, Lewis, &
Bradfield, 1999; Ashton, 1998; Coyne & Gentile, 2006; Fox & Spector, 1999; Gruys &
Sackett, 2003; Kelloway & Loughlin, 2002; Le et al., 2011; Peng, 2012; Spector et al., 2006;
Spector & Fox, 2002; Stewart, Bing, Davison, Woehr, & McIntyre, 2009; k for B5 domain
114 CHAPTER 4 scales = 17 – 29; k for HEXACO domain scales = 8 – 9) was used to assess workplace deviance.
Source of workplace deviance rating (self vs. other). To rate workplace deviance, studies used either self-report measures (k for B5 domain scales = 23 – 46) or other-report measures (k for B5 domain scales = 7 – 12). Only one study used an objective measure of workplace deviance (Kluemper et al., 2013). When a study reported both self- and other- ratings (e.g., Spector & Che, 2014), we included the self-rating in the overall analysis, but mention the results with other-ratings included in the overall analysis as well. For the
HEXACO, no study included other-ratings of workplace deviance.
Data Analysis
The Pearson product moment correlation coefficient (r) between one of the Big Five or HEXACO personality domain scales and workplace deviance was used as the effect size.
Cohen (1988) stated that r can be interpreted as a small (r = .10), medium (r = .30), or large (r
= .50) value. Comprehensive Meta-Analysis Software (CMA; Biostat, USA) was used to conduct the analyses for this study. Based on the assumption that we did not sample all studies available and that heterogeneity was present in the sample of effect sizes, a random effects model (REM) with inverse-variance weights was used (Borenstein, Hedges, Higgins,
& Rothstein, 2009). Using the REM, it is assumed that the true effect size varies from study to study, and the summary effect is the estimate of the mean of the distribution of effect sizes.
The steps CMA performs for this meta-analysis are as follows:
1. All studies included in this meta-analysis reported the effect size (r) and their sample
size (N). Because the variance depends on the magnitude of the correlation coefficient
(r), CMA converts the correlations to Fisher’s z. All analyses are performed using
Fisher’s z. After the analysis, the results are converted back to correlations.
2. The weight assigned to each study, and therefore to each Fisher’s z value, is the
inverse of that study’s variance. Since a REM is used, the variance is the sum of the
115 PERSONALITY AND WORKPLACE DEVIANCE
2 within-study variance (Vyi) and the between-studies variance (τ ) (DerSimonian &
Laird, 1986)
3. By means of a REM, the overall mean weighted effect size of all studies is computed.
The overall r for this meta-analysis is the sum of the product of all r, converted to
Fisher’s z value, and weights divided by the sum of all weights.
To assess whether the variation between observed correlations was due to real heterogeneity between studies and not because of within-study error, a Q statistic and an I2 index were computed, where I2 = [(Q-df)/Q] x 100% with df = k – 1 and k = number of effect sizes (Borenstein et al., 2009). The I2 is the proportion of the observed variance that reflects real, rather than chance, differences between effect sizes. Higgins and colleagues (2003) provided benchmark values for the interpretation of I2: 25%, 50%, and 75% might be considered as low, moderate, and high, respectively. To guarantee that the effect sizes are independent, the effect sizes for overall workplace deviance, ID, and OD were combined for the overall analysis if a study measured two or more of those forms of workplace deviance.
Publication Bias
In science, studies that report significant effect sizes are more likely to be published than studies that report non-significant effect sizes (Borenstein et al., 2009). Such publication bias can result in an overestimation of the true effect size. Publication bias would be present if precision and the study effect sizes differ significantly according to Begg and Mazumdar’s
(1994) rank correlation and Egger’s regression intercept (Egger et al., 1997). We also assessed the likelihood of publication bias using Duval and Tweedie's (2000) trim-and-fill method which mathematically corrects for asymmetry in the funnel plot of standard error by
Fisher’s z. However, this method has been seriously criticized because it not only corrects for publication bias that does not exist, resulting in underestimated effect sizes (Terrin et al.,
2003), but also because it does not correct for publication bias that does exist, resulting in overestimated effect sizes (Carter et al., 2017). Results of the trim-and-fill method should therefore be interpreted with caution.
116 CHAPTER 4
Moderator Analyses
The moderator analyses (subgroup analyses and univariate meta-regressions) in a mixed-effects model were used to test the influence of systematic variations in study characteristics on the overall weighted effect size. The source of the workplace deviance rating (self versus other), the workplace deviance questionnaire (Bennett & Robinson, 2000, versus other), and the personality questionnaire for the B5 (Big Five versus FFM) were included as categorical moderators.6 The percentage of women, the average age of the sample, and the number of items used to assess personality were included as continuous moderators.
Two-Stage Meta-Analytical Structural Equation Modeling
A two-stage random-effects meta-analytical structural equation modeling (MASEM) was conducted to calculate the variance in workplace deviance explained by the B5 and the
HEXACO personality model (Cheung, 2014, 2015). For all studies measuring the correlations between overall workplace deviance and all five B5 personality domain scales (k = 12) or all six HEXACO personality domain scales (k = 11), the correlations between the personality domain scales were coded as well. When the sample size differed per correlation, the lowest
(most conservative) number of participants was coded. All studies included in the MASEM analysis and their corresponding coded effect sizes are listed in Table 5 for B5 and in Table 6 for HEXACO.
MASEM combines meta-analysis with structural equation modeling and consists of two stages: in the first stage, the correlations between all variables from all primary studies are synthesized into an overall correlation matrix weighted by sample size. In the second stage, this meta-analytic correlation matrix is subjected to a structural equation model to calculate the explained variance in workplace deviance. In our study, two estimates of explained variance resulted from this test: the variance in workplace deviance explained by the B5 model, and the variance in workplace deviance explained by the HEXACO model.
117 PERSONALITY AND WORKPLACE DEVIANCE
118 CHAPTER 4
119 PERSONALITY AND WORKPLACE DEVIANCE
Results
Personality Predicting Overall Workplace Deviance
Results for the meta-analytic relation between each B5 and HEXACO personality domain scale and workplace deviance are shown in Table 7. Consistent with our expectation, out of all eleven personality domain scales, HEXACO Honesty-Humility showed the strongest correlation with workplace deviance (r = -.404, p < .001, k = 16). Both,
Conscientiousness (B5 r = -.281, p < .001, k = 54; HEXACO r = -.354, p < .001, k = 14) and
Agreeableness (B5 r = -.274, p < .001, k = 46; HEXACO r = -.161, p < .001 k = 13) were also significant predictors of workplace deviance. The correlations between Extraversion and workplace deviance (B5 r = -.028, p = .353, k = 29; HEXACO r = -.026, p = .488, k = 13) and
Openness to Experience and workplace deviance (B5 r = -.059, p < .05, k = 28; HEXACO r =
-.063, p = .284, k = 13) were either non-significant or so small that they were negligible. B5
Neuroticism (r = .142, p < .001, k = 42) and HEXACO Emotionality (r = -. 106, p < .01, k =
13) correlated significantly with workplace deviance, and in opposite directions.7
In line with our expectations, we did not find differences in effect sizes for B5 and
HEXACO Openness to Experience, Conscientiousness, and Extraversion (see Table 7 for the
Q-values statistically comparing the two effect sizes with each other). For Agreeableness, the effect size was significantly more negative for the B5 compared to the HEXACO, Q(1) =
23.231, p < .001. The correlations of B5 Neuroticism and HEXACO Emotionality with workplace deviance also differed significantly, Q(1) = 42.578, p < .001.
120 CHAPTER 4
Q
.946 .103 .963 .000 .000 for
p
) df (
Comparisons
Q 0.005 (1) 2.655 (1) 0.002 (1) 42.578 42.578 (1) 23.231 23.231 (1)
2 T .014 .040 .017 .025 .022 .013 .015 .000 .014 .009 .009
T .118 .201 .132 .158 .148 .115 .121 .000 .120 .095 .096 size weighted mean size observed -
Heterogeneity 2 I 0.000 = = sample
85.086 93.192 89.606 89.331 89.866 81.771 87.941 87.345 75.197 79.271 r
p .021 .284 .000 .000 .353 .488 .000 .000 .000 .001 .000
UL .009 .052 .246 .273 .031 .047 .237 .133 .182 .044 .356 ------CV
LL Size .108 .177 .316 .430 .086 .098 .309 .188 .101 .168 .450 ------CV
r .059 .063 .281 .354 .028 .026 .274 .161 .142 .106 .404 ------Overall Effect Effect Overall
N 4838 5127 4838 4838 4838 7053 12297 27471 12633 23709 20495
k 28 13 54 14 29 13 46 13 41 13 16 = lower and upper bounds of the 95% confidence interval; B5 = Big five, H = HEXACO of= H interval;= upper lower = Big five, the bounds and confidence B5 95%
UL
and and CV
LL
Humility - = number of statistically independent samples; N independent = samples; statistically cumulative = of size; sample number
Analytic Results for Overall Workplace Deviance Workplace Results for Analytic Overall k
- . Conscientiousness Neuroticism Extraversion able able 7 Domain scale B5 Openness Experience to H Openness B5 H Conscientiousness B5 Extraversion H B5 Agreeableness H Agreeableness B5 H Emotionality H Honesty T Meta Note correlation; CV
121 PERSONALITY AND WORKPLACE DEVIANCE
Publication Bias Analysis
The results of the publication bias analyses for all B5 and the HEXACO personality domain scales can be found in Table 8. The funnel plot for all B5 domain scales except
Agreeableness showed signs of asymmetry, and Egger's regression intercept (1997) was significant for all B5 personality domain scales except for Openness. Begg and Mazumdar's rank correlation (1994) was only significant for Neuroticism. However, the interpretation of the newly estimated effect sizes using the trim-and-fill method (Duval & Tweedie, 2000) was not substantially different for any of the B5 personality domain scales. Overall, it can be concluded that publication bias is unlikely to have had a strong influence on our meta-analytic findings for the B5. For HEXACO Conscientiousness the funnel plot was symmetric and no studies were imputed using the trim-and-fill method (Duval & Tweedie, 2000). Both, Begg and Mazumdar's rank correlation (1994) an Egger's regression intercept (1997) were also non- significant for this personality domain scale, suggesting that publication bias was not present.
For the HEXACO domain scales Honesty-Humility, Emotionality, Extraversion,
Agreeableness, and Openness to Experience, a few studies (1 – 3) were imputed using the trim-and-fill method (Duval & Tweedie, 2000), but this did not change the interpretation of the overall weighted effect size for any of those personality domain scales. Begg and
Mazumdar's rank correlation (1994) was non-significant for all HEXACO personality domain scales, and Egger's regression intercept (1997) was statistically significant only for
Extraversion and Openness to Experience. Overall, it is very unlikely that publication bias strongly influenced the results for the HEXACO personality domain scales.
122 CHAPTER 4
Table 8 Publication Bias Analyses Results Big Five and HEXACO D&Tleft D&Tright Adjusted ES B&M Egger B5 Openness 6 --- -.098 .418 .152 B5 Conscientiousness 13 --- -.332 .081 .000 B5 Extraversion 9 --- -.092 .081 .007 B5 Agreeableness ------.075 .008 B5 Neuroticism 1 --- .136 .046 .002
H Honesty-Humility --- 3 -.376 .558 .939 H Emotionality --- 2 -.080 .360 .225 H Extraversion 2 --- -.049 .583 .043 H Agreeableness 2 --- -.169 .669 .421 H Conscientiousness ------.913 .233 H Openness to Experience 1 --- -.082 .669 .009 Note. D&T = Duwal and Tweedie’s trim-and-fill approach for the funnel plot; D&Tleft = imputed studies to the left of the overall mean weighted effect size; D&Tright = imputed studies to the right of the overall mean weighted effect size; Adjusted ES = adjusted effect size after imputing studies using Duwal & Tweedie’s trim-and-fill approach; B&M = p-value of the Begg and Mazumdar’s rank correlation test; Egger = p-value of the Egger’s regression intercept test; B5 = Big Five, H = HEXACO.
Differential Prediction of ID and OD
As can be seen in Table 9 (B5) and Table 10 (HEXACO), subgroup analyses revealed that none of the eleven personality domain scales (B5 and HEXACO) correlated differently with ID or OD. This contradicts our expectations and previous meta-analytic results based on a smaller number of effect sizes by Berry and colleagues (2007), who found that B5
Agreeableness more strongly correlates with ID than with OD, B5 Conscientiousness more strongly correlates with OD than with ID, and B5 Extraversion more strongly and negatively with OD (see also Table 11 for a comparison of our results with those from previous meta- analyses).
123 PERSONALITY AND WORKPLACE DEVIANCE
Table 9 Meta-Analytic Results of Big Five and WD Domain Scales: ID and OD k (N) r CVLL CVUL p Q (df) p for Q Openness ID 16 (3706) -.048 -.099 .002 .060 OD 15 (3606) -.030 -.070 .011 .152 0.321 (1) .571
Conscientiousness ID 30 (8546) -.244 -.293 -.194 .000 OD 33 (9567) -.284 -.331 -.235 .000 1.247 (1) .264
Extraversion ID 16 (3706) -.000 -.063 .062 .988 OD 15 (3606) -.012 -.069 .045 .673 0.075 (1) .785
Agreeableness ID 30 (8352) -.259 -.312 -.206 .000 OD 29 (8423) -.257 -.300 -.213 .000 0.004 (1) .952
Neuroticism ID 23 (6258) .159 .090 .227 .000 OD 24 (6624) .140 .094 .185 .000 0.209 (1) .647 Note. ID = interpersonal workplace deviance; OD = organizational workplace deviance; k = number of statistically independent samples; N = total sample size; r = sample-size weighted mean observed correlation; CVLL and CVUL = lower and upper bounds of the 95% confidence interval.
Table 10. Meta-Analytic Results of HEXACO and WD Domain Scales: ID and OD k (N) r CVLL CVUL p Q (df) p for Q Honesty-Humility ID 4 (1509) -.392 -.505 -.265 .000 OD 5 (1623) -.412 -.480 -.339 .000 0.081 (1) .776
Emotionality ID 2 (778) -.074 -.245 .102 .411 OD 3 (892) -.087 -.225 .054 .226 0.014 (1) .906
Extraversion ID 2 (778) -.105 -.174 -.035 .003 OD 3 (892) -.139 -.251 -.024 .018 0.254 (1) .614
Agreeableness ID 2 (778) -.198 -.264 -.129 .000 OD 3 (892) -.152 -.216 -.087 .000 0.992 (1) .337
Conscientiousness ID 2 (778) -.485 -.608 -.339 .000 OD 3 (892) -.399 -.628 -.107 .009 0.335 (1) .563
Openness to Experience ID 2 (778) -.342 -.402 -.278 .000 OD 3 (892) -.203 -.377 -.016 .034 2.081 (1) .149 Note. ID = interpersonal workplace deviance; OD = organizational workplace deviance; k = number of statistically independent samples; N = total sample size; r = sample-size weighted mean observed correlation; CVLL and CVUL = lower and upper bounds of the 95% confidence interval.
124 CHAPTER 4
Comparison of Current Results with Previous Meta-Analytic Results (B5)
Three previous meta-analyses have examined the relations between B5 domain scales and workplace deviance (Berry et al., 2012, 2007; Salgado, 2002). The effect sizes found in these previous meta-analyses and the effect sizes from the current meta-analysis are shown in
Table 11. The most notable finding is that the magnitude of the overall weighted correlations for B5 Conscientiousness (r = -.28 compared to Salgado, 2002: r = -.16) and Agreeableness (r
= -.27 compared to Salgado, 2002: r = -.13) are much larger in the current meta-analysis than in Salgado’s (2002) meta-analysis. This difference might be due to the narrower focus of deviant behaviors applied by Salgado (2002). This difference does not hold when comparing the current results to the effect sizes found by Berry et al. (2012) for self-reported workplace deviance (B5 Conscientiousness: r = -.28 compared to Berry et al. (2012): r = -.31; B5
Agreeableness: r = -.27 compared to Berry et al. (2012): r = -.35). In addition, the magnitude of the overall weighted correlation for Neuroticism (r = .14 compared to Salgado, 2002: r =
.04 and Berry et al., 2012: r = .23) is also substantially different in the current meta-analysis, whereas the magnitude of the overall weighted effect sizes are relatively similar to the previous estimates for Openness to Experience (r = -.06 compared to Salgado, 2002: r = .10 and Berry et al., 2012: r = -.06) and Extraversion (r = -.03 compared to Salgado, 2002: r = .01 and Berry et al., 2012: r = -.03). Given that the current meta-analysis is based on a much larger sample of studies, it seems reasonable to place more confidence in these newer estimates
Moderator Analyses
The results of the categorical moderator analyses can be found in Table 12 (B5) and
Table 13 (HEXACO), and the results for the continuous meta-regressions can be found in
Table 14 (B5 and HEXACO).
125 PERSONALITY AND WORKPLACE DEVIANCE
are
) k
.03 (5) .34 (8) .07 (5) .25 (8) - - - - .19* (7) =
k OD (
) k .07 (8) .02 (8) - .19 (11) .36 (10) Berry et (2007) Berry al. - - ) is not reported in ) this in reported is not .20* (10) ID ( k
reported workplace deviance
- ) k sed the sed correlations for Salgado .03 (15) .28 (33) .01 (15) .26 (29) .14 (24) - - - -
OD (
This study ) k icism from Berry et al. (2007) and Berry et al. Berry Berry al. icism et from (2007) al. et and .05 (16) .24 (30) .00 (16) .26 (30) .16 (23) - - - - ID ( deviant behavior was measured (this measured was not does apply to deviant behavior
Analyses B5)(only Analyses for lack lack of a - .06 .31 .03 .35 - - - - .23*
a (2012) reported WD reported - Berry et Berry al. Self
size weighted correlations. we Note rever that * - .10 (8) .13 (9)
- .16 (13) .01 (12) .04 (15) - )* Analysis and Meta Previous Analysis k - ( The correlations between personality domain scales Theand self correlations scales personality between domain
a Salgado (2002) Salgado (2002)
analysis. analysis.
- ) k .06 (28) .28 (54) .03 (29) .27 (46) .14 (41) - - - - analysis the relations analysis personality between and a - This ( study
. WD = Workplace Deviance; O = Openness; C = Conscientiousness; E . N C = = = Neuroticism.Conscientiousness; = = WD Agreeableness; Openness; = Extraversion; A O Workplace Deviance;
B5 domain B5 scale domain able able 11 O C E A N T of the Comparison Results of Meta the Current Note number included in studies of meta perreported The from ( (2007).on Berrystudies al. (2012) Berry of al. effect butbased number data in et et are size article.These correlations sample are uncorrected, mean (2002)meta in this because Neuroticism,Salgado (2002) Emotional and measured Stability), because for B5 Neurot (2012).
126 CHAPTER 4
Table 12 Results of the Categorical Moderator Analyses for the B5 k (N) r CVLL CVUL p Q (df) p for Q B5 Openness WD Questionnaire 2.450 (1) .118 Bennett & Robinson (2000) 11 (7437) -.108 -.196 -.019 .017 Other 17 (4860) -.028 -.075 .019 .239 WD rater 0.981 (1) .322 Self 23 (11366) -.054 -.108 .001 .055 Other 7 (1468) -.107 -.197 -.016 .021 Personality questionnaire 3.286 (1) .070 FFM 6 (2562) -.148 -.260 -.033 .012 BFI 21 (9201) -.034 -.079 .010 .130 B5 Conscientiousness WD Questionnaire 8.730 (1) .003 Bennett & Robinson (2000) 25 (15938) -.331 -.374 -.286 .000 Other 29 (11533) -.237 -.280 -.192 .000 WD rater 19.495 (1) .000 Self 46 (24830) -.306 -.340 -.272 .000 Other 12 (3738) -.173 -.221 -.124 .000 Personality questionnaire 0.009 (1) .924 FFM 8 (3636) -.278 -.406 -.139 .000 BFI 39 (18919) -.285 -.322 -.247 .000 B5 Extraversion WD Questionnaire 0.055 (1) .815 Bennett & Robinson (2000) 11 (7437) -.040 -.151 .072 .482 Other 18 (5196) -.025 -.082 .032 .390 WD rater 0.323 (1) .570 Self 24 (11702) -.037 -.102 .028 .262 Other 7 (1468) -.061 -.112 -.010 .020 Personality questionnaire 0.001 (1) .981 FFM 6 (2562) -.044 -.257 .174 .697 BFI 22 (9537) -.041 -.079 -.003 .036 B5 Agreeableness WD Questionnaire 13.240 (1) .000 Bennett & Robinson (2000) 23 (14819) -.332 -.378 -.285 .000 Other 23 (8890) -.209 -.256 -.162 .000 WD rater 0.434 (1) .510 Self 38 (22267) -.284 -.319 -.247 .000 Other 10 (1979) -.248 -.347 -.143 .000 Personality questionnaire 0.038 (1) .845 FFM 9 (4484) -.284 -.370 -.193 .000 BFI 34 (16973) -.274 -.316 -.232 .000 B5 Neuroticism WD Questionnaire 0.140 (1) .708 Bennett & Robinson (2000) 15 (10188) .153 .078 .227 .000 Other 26 (10307) .137 .093 .180 .000 WD rater 0.870 (1) .351 Self 33 (17854) .154 .110 .198 .000 Other 11 (3324) .116 .047 .183 .001 Personality questionnaire 0.011 (1) .917 FFM 6 (2562) .158 .015 .295 .030 BFI 30 (14197) .151 .109 .192 .000 Note. k = cumulative number of studies; r = sample size weighted correlation; CVLL and CVUL = lower and upper bounds of the 95% confidence interval; WD = Workplace deviance; WD rater = source of the workplace deviance rating; B5 = Big Five; BFI = personality questionnaire based on the Big Five Inventory (i.e., Goldberg, 1990); FFM = personality questionnaire based on the Five-Factor Model (McCrae & Costa, 1992).
127 PERSONALITY AND WORKPLACE DEVIANCE
Table 13 Results of the Categorical Moderator Analyses for the HEXACO k (N) r CVLL CVUL p Q (df) p for Q H Honesty-Humility WD Questionnaire 0.193 (1) .660 Bennett & Robinson (2000) 7 (4790) -.415 -.482 -.343 .000 Other 9 (2263) -.394 -.458 -.325 .000 H Emotionality WD Questionnaire 0.262 (1) .609 Bennett & Robinson (2000) 5 (2864) -.087 -.187 .015 .095 Other 8 (1974) -.121 -.204 -.036 .005 H Extraversion WD Questionnaire 21.375 (1) .000 Bennett & Robinson (2000) 5 (2864) -.155 -.227 -.080 .000 Other 8 (1974) .050 .006 .095 .026 H Agreeableness WD Questionnaire 0.979 (1) .323 Bennett & Robinson (2000) 5 (2864) -.175 -.210 -.139 .000 Other 8 (1974) -.143 -.194 -.092 .000 H Conscientiousness WD Questionnaire 5.880 (1) .015 Bennett & Robinson (2000) 6 (3153) -.445 -.544 -.334 .000 Other 8 (1974) -.279 -.354 -.202 .000 H Openness to Experience WD Questionnaire 11.678 (1) .001 Bennett & Robinson (2000) 5 (2894) -.213 -.319 -.102 .000 Other 8 (1974) .025 -.054 .105 .531 Note. k = cumulative number of studies; N = cumulative sample size; r = sample size weighted correlation; CVLL and CVUL = lower and upper bounds of the 95% confidence interval; H = HEXACO.
128 CHAPTER 4
Table 14 Results of the Continuous Meta-Regressions 2 k Slope R Slope ptwo-tailed B5 Openness Average age 26 -.005 .00 .205 % Women 26 -.001 .00 .794 # items 26 .003 .00 .534 H Openness to Experience Average age 12 -.009 .00 .285 % Women 13 .009 .58 .006 # items 12 .016 .33 .042 B5 Conscientiousness Average age 49 .001 .00 .712 % Women 49 -.002 .09 .178 # items 49 .001 .00 .799 H Conscientiousness Average age 13 .001 .00 .887 % Women 14 .003 .00 .417 # items 13 .004 .04 .503 B5 Extraversion Average age 27 -.004 .00 .338 % Women 26 -.002 .01 .468 # items 27 .004 .00 .459 H Extraversion Average age 12 -.009 .03 .061 % Women 13 .005 .52 .011 # items 12 .006 .07 .306 B5 Agreeableness Average age 41 -.000 .00 .919 % Women 42 -.005 .22 .004 # items 42 -.001 .00 .767 H Agreeableness Average age 12 .001 .00 .751 % Women 13 .002 .00 .060 # items 12 -.002 .00 .526 B5 Neuroticism Average age 37 .006 .11 .031 % Women 36 .001 .07 .500 # items 38 -.000 .00 .994 H Emotionality Average age 12 .009 .22 .024 % Women 13 -.002 .00 .444 # items 12 -.003 .00 .518 H Honesty-Humility Average age 14 .008 .27 .036 % Women 15 .003 .21 .142 # items 14 .001 .00 .902 Note. k = cumulative number of studies; N = cumulative sample size; H = HEXACO, B5 = Big Five; Average Age = the average age of participants in all included studies; % Women = the average percentage of women in all included studies; # items = the number of items used to assess the respective personality domain scale.
129 PERSONALITY AND WORKPLACE DEVIANCE
The personality questionnaire used, based either on Goldberg (1990) for the B5 or on
McCrae and Costa (1992) for the FFM, did not moderate the relation between any of the B5 personality domain scales and workplace deviance. Hence, our approach of combining the B5 and FFM domain scales seemed to be valid. The number of items used to assess a personality domain scale also did not moderate the relations between most personality domain scales and workplace deviance; it was only significant for HEXACO Openness to Experience.
The questionnaire used to assess workplace deviance only moderated the relations between personality and workplace deviance for the following domain scales: B5
Conscientiousness, B5 Agreeableness, HEXACO Extraversion, HEXACO Conscientiousness, and HEXACO Openness to Experience. The relations were stronger and more negative for all of these personality domain scales when Bennett and Robinson's (2000) workplace deviance questionnaire, compared to all other questionnaires, was used. The relations with workplace deviance for the remaining domain scales were not moderated by the questionnaire used to assess workplace deviance.
The source of the workplace deviance rating significantly moderated the relation between B5 Conscientiousness and workplace deviance. B5 Conscientiousness showed a significantly stronger correlation with self-ratings (r = -.306, k = 46) than with other-ratings of workplace deviance (r = -.173, k = 12). For all four other B5 personality domain scales, the source of the workplace deviance rating did not moderate the relation of interest. This contradicts previous meta-analytic findings, as Berry et al. (2012) report notable differences in the relations of personality with self- and other-reports of workplace deviance. This moderation effect could not be tested for the HEXACO because no study measured the relations with other-reports of workplace deviance.
The percentage of women included in each study moderated the relations between B5
Agreeableness, HEXACO Openness to Experience, and HEXACO Extraversion and workplace deviance. For HEXACO Openness to Experience (k = 13, slope = .009, p < .01) and HEXACO Extraversion (k = 13, slope = .005, p < .05) the effect size became more
130 CHAPTER 4 positive with an increasing percentage of women in the included studies, whereas it became more negative for B5 Agreeableness (k = 42, slope = -.005, p < .01). For the remaining eight personality domain scales, the percentage of women included in each study did not moderate the relations between the respective personality domain scale and workplace deviance.
The average age of the participants in each study only moderated the relations between the domain scales B5 Neuroticism, HEXACO Emotionality, and HEXACO Honesty-
Humility, and workplace deviance. The effect size became more positive/less negative with increasing average age of participants in the included studies for all three personality domain scales: B5 Neuroticism (k = 37, slope = .006, p < .05), HEXACO Emotionality (k = 12, slope
= .009, p < .05), and HEXACO Honesty-Humility (k = 14, slope = .008, p < .05). For the remaining eight personality domain scales, the average age of participants in each study did not moderate the relations between the respective personality domain scales and workplace deviance.
Comparing the B5 and the HEXACO in Predicting Workplace Deviance
We conducted a two-stage MASEM to compare the variance that is explained by either the B5 or the HEXACO in workplace deviance. The overall weighted correlation matrices synthesized in the first step can be found in Table 15 (B5) and in Table 16
(HEXACO). Because we only included studies that measured the relations between all personality domain scales and workplace deviance to ensure the validity of the MASEM approach, the number of included studies here is lower than in the overall meta-analysis (k =
12 for B5, k = 11 for HEXACO). However, the overall weighted effect sizes for each personality domain scale with workplace deviance closely resemble those we found when including all available studies (see Table 7, 15, and 16). In the second stage, we fitted a structural equation model with all personality domain scales predicting workplace deviance.
Results show that the B5 personality domain scales explained about 17.3% of the variance in workplace deviance, k = 12, N = 4970, R2 = .171, 95% CI for R2 (.127; .224)8, whereas the
HEXACO personality domain scales explained about 24.9% of the variance in workplace
131 PERSONALITY AND WORKPLACE DEVIANCE deviance, k = 11, N = 2683, R2 = .249, 95% CI for R2 (.203; .305). Hence, the HEXACO explained 7.6% more workplace deviance variance than the B5.
Table 15 Correlation Matrix for Workplace Deviance and the B5 Personality Domain scales WD O C E A N WD - O -.077 - C -.339 .168 - E -.081 .236 .148 - A -.304 .218 .291 .270 - N .208 -.143 -.253 -.272 -.229 - Note. k = 12, N = 4970; WD = Workplace deviance, O = Openness; C = Conscientiousness; E = Extraversion; A = Agreeableness; N = Neuroticism.
Table 16 Correlation Matrix for Workplace Deviance and the HEXACO Personality Domain scales WD H E X A C O WD - H -.435 - E -.115 .059 - X -.019 .002 -.079 - A -.140 .292 .164 .142 - C -.361 .331 .052 .132 .117 - O -.078 .165 -.092 .210 .115 .136 - Note. k = 11, N = 2683; WD = Workplace deviance, H = Honesty-Humility, E = Emotionality, X = Extraversion, A = Agreeableness, C = Conscientiousness, O = Openness to Experience.
Discussion
In an effort to provide a comprehensive overview of the relations between personality and workplace deviance, the current study is the first to meta-analytically compare the B5 with the HEXACO in predicting workplace deviance, and to the best of our knowledge in predicting any organizational outcome. Our results indicate that when predicting workplace deviance, the HEXACO model outperforms the B5 model. Furthermore, Honesty-Humility was the strongest predictor of workplace deviance out of all eleven personality domain scales included in this meta-analysis. This finding underlines the importance of a personality domain scale which taps directly into individual differences in the propensity for exploitation and
132 CHAPTER 4 deception (i.e., Honesty-Humility) (Ashton, 2000; Lee, Ashton, & Shin, 2005; Ashton, Lee, &
Son, 2000), at least in the prediction of workplace deviance. Considering the ubiquity of personality questionnaires in employee selection contexts (Ryan et al., 2015) and the fact that supervisor’s overall job performance ratings depend heavily on workplace deviance ratings
(as much as task performance ratings and more than OCB ratings; Dunlop & Lee, 2004;
Rotundo & Sackett, 2002), this meta-analysis suggests that it is important to capture variance associated with Honesty-Humility in employee selection contexts. The current results also suggest that Conscientiousness and Agreeableness (Agreeableness somewhat weaker in the
HEXACO model), and to a lesser extent B5 Neuroticism and HEXACO Emotionality, are important predictors of workplace deviance. Openness to Experience and Extraversion (for both B5 and HEXACO) do not seem to play a major role in the prediction of workplace deviance.
Comparison of B5 and HEXACO Personality Domain scales
No significant differences in relations with workplace deviance between the B5 and
HEXACO personality domain scales of Openness to Experience, Conscientiousness, and
Extraversion were observed. This was expected because these personality domain scales are conceptually similar in the B5 model and the HEXACO model (Lee & Ashton, 2004). B5
Agreeableness correlated more strongly with workplace deviance than HEXACO
Agreeableness. This likely reflects the fact that B5 (and especially FFM) Agreeableness captures some variance associated with HEXACO Honesty-Humility, which correlates most strongly with workplace deviance. This apparently outweighs the effect of a missing
(reversed) anger facet in B5 Agreeableness, which has been shown to correlate with workplace deviance (Hastings & O’Neill, 2009) and which is part of HEXACO
Agreeableness.
Some may see in the above results confirmation of the position, advocated by some B5 researchers (DeYoung, 2015; Viswesvaran & Ones, 2016), that Honesty-Humility is not much more than a facet of Agreeableness. However, such a position negates the findings of this
133 PERSONALITY AND WORKPLACE DEVIANCE meta-analysis that HEXACO Honesty-Humility already explains almost twice the amount of variance explained by B5 Agreeableness in workplace deviance (i.e., 18.9% versus 9.5%).
Furthermore, such a position also negates findings in this and other studies that a) HEXACO
Honesty-Humility and Agreeableness are only moderately related (i.e., r = .29 in this study and a correlation of .28 between Honesty-Humility and B5 Agreeableness in Ashton et al.
(2014)) and that b) HEXACO Honesty-Humility and Agreeableness have significant different predictive validities for a great number of important other variables, such as—among others—values and political orientations (Lee et al., 2009; Lee, Ashton, Ogunfowora,
Bourdage, & Shin, 2010), the Dark Triad (Lee et al., 2013; Lee & Ashton, 2014), and several economic (public good and social dilemma) games (Hilbig et al., 2016, 2013; Zhao & Smillie,
2015). This, together with the finding that the most recent large-scale cross-cultural lexical studies offer support for separate Agreeableness and Honesty-Humility dimensions (Ashton et al., 2004; De Raad et al., 2014; Saucier, 2009), seems to indicate that the B5 model omits a highly important and consequential variable.
The relations between workplace deviance and B5 Neuroticism and HEXACO
Emotionality differed significantly and in direction, offering support for the conceptual distinction between these two domain scales. B5 Neuroticism includes content associated with anger, which has been found to be positively related to workplace deviance (Hastings &
O’Neill, 2009), whereas HEXACO Emotionality includes content associated with anxiety and sentimentality, which has been found to be somewhat negatively related to (workplace) deviance (e.g., Van Gelder & De Vries, 2012). Overall, these findings provide criterion- related support for the conceptual similarities and differences between the B5 and the
HEXACO. While the current results suggest that the B5 personality model is useful in the prediction of workplace deviance, the results also suggest that practitioners and researchers might like to use the HEXACO instead of the B5 personality model because of the inclusion of the Honesty-Humility domain scale and because of the higher level of explained variance in workplace deviance by the HEXACO model when compared to the B5 model. In
134 CHAPTER 4 particular, practitioners and researchers are advised to include the personality domain scales of Honesty-Humility, Conscientiousness, Agreeableness, and Emotionality when their goal is to predict behaviors associated with workplace deviance. These findings also align with previous findings suggesting that the HEXACO personality model, compared to the B5 personality model, better predicts various criteria in- and outside the workplace, such as cooperation (Thielmann & Hilbig, 2014), unethical leadership (De Vries, 2012), and delinquent and criminal behaviors (De Vries & Van Gelder, 2013, 2015).
Comparison with Previous Meta-Analytic Findings
Contrary to previous meta-analytic results (Berry et al., 2007), none of the personality domain scales correlated differently with the two facets of workplace deviance, ID and OD.
This contradicts Berry and colleagues’ (2007) finding that Agreeableness correlated more strongly with ID and Conscientiousness more strongly with OD. It seems that personality domain scales predict overall levels of workplace deviance, but do not differentially predict specific facets of deviant behaviors. This finding might reflect the fact that certain personality traits incline individuals to be prone to exhibit deviant behavior independently of who or what the target is. At least when using personality domain scales as predictor variables, differentiating between the two facets of workplace deviance seems redundant. In combination with at least one influential study failing to replicate the two-factor structure of workplace deviance (Lee & Allen, 2002), these results may further question the viability of such a two-factor structure.
Other findings in the current meta-analysis also differ notably from previous meta- analyses (see Table 11; Berry et al., 2012, 2007; Salgado, 2002). Most importantly, the overall weighted correlation coefficients found in the current meta-analysis were notably different for B5 Conscientiousness, Agreeableness, and Neuroticism from previous meta- analyses (Berry et al., 2012; Salgado, 2002). Our results might differ from Salgado’s (2002) results because of the narrower conceptualization of workplace deviance in his meta-analysis, which might have led to generally smaller correlations. The overall weighted effect sizes did
135 PERSONALITY AND WORKPLACE DEVIANCE not differ substantially for B5 Openness to Experience and Extraversion. Another notable difference is that, except for B5 Conscientiousness, self- and other-reports of workplace deviance do not correlate differently with personality. Yet, Berry et al. (2012) report quite substantial differences between self- and other-reports (r = -.07 to .18). Given that the current meta-analysis is based on a much larger sample than previous ones, it seems that more confidence can be placed in these results. On the other hand, some might argue that our findings reflect an increased interest in personality as a research field, making significant findings more likely to be published than non-significant ones (Borenstein et al., 2009).
However, the publication bias analyses performed in this meta-analysis did not indicate major problems.
Methodological Implications
The current findings carry important implications for the future study of personality and workplace deviance. The results remained robust independently of the source of the workplace deviance rating (except for B5 Conscientiousness, which showed a stronger correlation with self-ratings compared to other-ratings of workplace deviance). This may indicate that personality is equally valid in predicting self- and other-reported workplace deviance, and may demonstrate that the personality-workplace deviance relations do not suffer from common-method bias. However, the questionnaire used to assess workplace deviance significantly moderated the relations between workplace deviance and the personality domain scales of B5 Conscientiousness, B5 Agreeableness, HEXACO
Extraversion, HEXACO Conscientiousness, and HEXACO Openness to Experience. For all of these personality domain scales, the Bennett and Robinson (2000) measure showed a more negative correlation with workplace deviance than other measures. While this categorization of workplace deviance questionnaires simplifies the underlying differences between questionnaires, these findings might indicate that the Bennett and Robinson (2000) questionnaire inflates the relations between personality and workplace deviance or that it more optimally captures those behaviors (i.e., workplace deviance) that are associated with
136 CHAPTER 4 personality. Future research could investigate this in more detail, but it is important that researchers are aware of these differences between workplace deviance measures.
The number of items used to assess a personality domain scale also did not moderate the relations between personality and workplace deviance. This finding does not align well with findings that shorter scales with lower reliabilities demonstrate lower validities (e.g.,
Gosling, Rentfrow, & Swann Jr, 2003). Possible explanations for the lack of a moderating effect could be that studies with short scales might be especially prone to publication bias or that short scales contain items that more optimally capture the variance associated with workplace deviance. Age only influenced the relations between a few personality domain scales and workplace deviance (i.e., B5 Neuroticism, HEXACO Emotionality, and HEXACO
Honesty-Humility). A similar picture emerged for the average percentage of women in a respective study, which moderated the relations between workplace deviance and a few personality domain scales (i.e., HEXACO Openness to Experience, HEXACO Extraversion, and B5 Agreeableness). This might reflect gender differences in personality and in levels of workplace deviance (De Vries, Ashton, & Lee, 2009; Ng et al., 2016). However, no clear picture across personality domain scales emerged for these two continuous moderators, making it difficult to interpret these findings. Nonetheless, researchers and practitioners should be aware of these findings when examining these relations in age and gender diverse samples. Researchers might want to control for age and gender differences between participants when examining the relations between those personality domain scales and workplace deviance for which the relations were moderated by age and the percentage of women.
Practical Implications
Even though task performance is usually the main criterion in employee selection contexts, research indicates that workplace deviance is one of the main detrimental behaviors for organizational success (Dunlop & Lee, 2004), making the prediction of this additional criterion more and more important. The prediction of deviant behavior at work even enjoys
137 PERSONALITY AND WORKPLACE DEVIANCE one advantage over the prediction of task performance because workplace deviance is not limited to a specific job, but, just like OCB, cuts across tasks, jobs, and work environments
(Podsakoff, Whiting, Podsakoff, & Blume, 2009). The current meta-analysis clearly outlines that organizations are at an advantage if they can use personality questionnaires to select employees who lack a proneness for deviant behavior (Podsakoff et al., 2009). Whereas previous research has positioned Conscientiousness and Agreeableness as the main predictors of task performance (Barrick, Mount, & Judge, 2001; Ilies, Fulmer, Spitzmuller, & Johnson,
2009), the current meta-analysis suggests that these two personality domain scales in combination with Honesty-Humility and Emotionality (Neuroticism in the B5, but note the opposite relation) are most important in the prediction and prevention of workplace deviance.
When practitioners can choose between personality questionnaires, they might like to opt for the HEXACO personality inventory instead of one of the B5 questionnaires, as the HEXACO personality inventory is able to explain more variance in workplace deviance.
Practitioners can also use the current findings by applying trait activation theory
(TAT: Tett & Burnett, 2003). According to TAT, an individual’s traits, such as personality, are either activated or inhibited in response to trait-relevant cues in the situation (Tett &
Burnett, 2003). An ideal work situation is one that offers cues for trait expression and one where trait-expressive behavior is positively valued by others. Furthermore, evidence indicates that individuals actively seek situations that provide opportunities for expressing those traits that they are rewarded for (e.g., De Vries, Tybur, Pollet, & Van Vugt, 2016; Tett
& Christiansen, 2007), and the facilitating effect of negative experiences at work on workplace deviance can be increased or decreased by certain personality traits (Colbert et al.,
2004). To decrease levels of workplace deviance, organizations could think of ways to trigger or reward the expression of those personality traits that decrease the occurrence of workplace deviance (i.e., Honesty-Humility, Conscientiousness, Agreeableness, Emotionality). In addition, when predicting job performance, the validity of personality seems to be stronger in less structured jobs, and this effect might be enhanced in certain job contexts for certain
138 CHAPTER 4 personality domain scales (Judge & Zapata, 2015). For example, employees low on Honesty-
Humility are more likely to take advantage of a situation to enrich themselves at the cost of others when punishment is unlikely (Hilbig, Zettler, & Heydasch, 2012). This highlights the importance of considering personality domain scales in job selection contexts especially for unstructured jobs in which applicants have a lot of freedom to make their own decisions.
Limitations and Future Research
The current meta-analysis has some limitations. First, the moderator analyses for the
HEXACO should be interpreted with caution because these analyses are only based on a small number of effect sizes. It should also be stressed that the interpretation of the personality questionnaire (B5 versus FFM) needs to be interpreted with caution: for a few studies, it was not entirely clear if the authors used the B5 or the FFM questionnaires. Second, the data analyzed here is based on cross-sectional designs, which does not allow causality inferences. However, personality is assumed to be relatively stable (Larsen & Buss, 2005), which makes it unlikely that workplace deviance determines an individual’s personality. One way to overcome this issue would be to investigate the effects of personality on workplace deviance with longitudinal designs. Furthermore, while this study shows that personality is a strong predictor of workplace deviance, using broad personality domain scales instead of narrow facets to investigate the relations between personality and workplace deviance may suppress the actual effects of those facets. It has been previously argued that broad personality measures (domain scales) are less strongly correlated with workplace deviance than narrow measures (facets) (Ashton, 1998; Hastings & O’Neill, 2009). Combining narrow personality traits into overall personality domain scales may obscure true effects, because some facets of one domain scales might correlate positively with workplace deviance, whereas other facets of the same domain scales might correlate less strongly or even negatively with it. For example, Hastings and O’Neill (2009) found that the narrow Anger facet in Neuroticism correlated positively with workplace deviance (r = .28), whereas the Anxiety facet correlated negatively with it (r = -.07).9 These facets subsequently suppress each other. It can also be the
139 PERSONALITY AND WORKPLACE DEVIANCE case that different facets correlate differently with ID and OD. Unfortunately, not enough data was available to meta-analytically investigate this. Future research should therefore investigate the effects of personality facets on workplace deviance in more detail.
Conclusion
The current meta-analysis provides the first comprehensive overview of the relations between personality and workplace deviance and demonstrates that the HEXACO explains more variance in workplace deviance than the B5. The Honesty-Humility domain scale of the
HEXACO shows the strongest (negative) relation with workplace deviance out of all
HEXACO and B5 personality domain scales. Apart from Honesty-Humility,
Conscientiousness, Agreeableness, and Emotionality (Neuroticism) are also important predictors of workplace deviance. The findings in the current meta-analysis differ from those in previous meta-analyses (Berry et al., 2012, 2007; Salgado, 2002). That is, the magnitude of effect sizes for Conscientiousness, Agreeableness, and Neuroticism were notably different and none of the personality domain scales correlated differently with ID or OD. Overall, the current meta-analysis provides further evidence for the importance of personality in the prediction of workplace deviance.
140 CHAPTER 4
Footnotes
1 All correlations reported here and further below from previous meta-analyses (Berry et al., 2007, 2012; Salgado, 2002) are observed correlations.
2 Although Emotional Stability is the official term used in the Big Five personality model, we will refer to it as Neuroticism, which is the opposite pole of the Emotional Stability domain scale, to better align it directionally with HEXACO Emotionality.
3 The signs for the results reported in Salgado (2002) are reversed here because this meta-analysis measured the relations between personality and a lack of deviant behavior.
Salgado (2002) reports correlations that are corrected for range restriction.
4 These correlations refer to those with self-reported workplace deviance and are based on data from Berry et al. (2007), but are reported in Berry et al. (2012). It is not clear from
Table 5 in Berry et al. (2012) whether these correlations are corrected for unreliability or not.
5 The correlations reported here and further below from Berry et al. (2012) are mean sample-size weighted correlations. These authors also report correlations corrected for unreliability in both the predictor and criterion variables, but given that we do not correct for unreliability, we reported the uncorrected correlations here. Berry et al. (2012) did not test if the effect sizes for the personality domain scales differ in their relationship with ID and OD.
We tested the difference between the two correlation coefficients they report for each personality domain and found a significant difference for Conscientiousness (ID: r = -.19,
OD: r = -.34; z = -6.44, p < .001), Agreeableness (ID: r = -.36, OD: r = -.25; z = 4.80, p <
.001), and Extraversion (ID: r = .02, OD: r = -.07; z = 2.89, p < .01), but not for Neuroticism
(ID: r = .20, OD: r = .19; z = 0.37, p = .711) and Openness (ID: r = -.07, OD: r = -.03; z =
1.27, p = .204) (all p-values are two-tailed).
6 The personality questionnaire used for the HEXACO is always based on the same original questionnaire (Lee & Ashton, 2004), whereas most personality questionnaires for the
B5 were either based on Goldberg's (1990) Big Five Model or on McCrae and Costa's (1992)
Five-Factor Model. Hence, we could only examine the moderating effect of the personality
141 PERSONALITY AND WORKPLACE DEVIANCE questionnaire for the B5 personality domain scales. The same applies to the source of the workplace deviance rating, because no study that used the HEXACO included other-reports of workplace deviance.
7 As mentioned in the Method, we included the self-rating in the overall analysis if a study reported correlations between personality and both self- and other-ratings of workplace deviance to guarantee the independence of effect sizes. However, the results do not substantially change if the other-rating of workplace deviance is included in the overall analysis instead. The results with other-ratings of workplace deviance included are as follows:
B5 Openness: r = -.056, 95% CI (-.104, -.007), p < .05; B5 Conscientiousness: r = -.271, 95%
CI (-.308, -.234), p < .001; B5 Extraversion: r = -.033, 95% CI (-.091, .025), p = .263; B5
Agreeableness: r = -.264, 95% CI (-.300, -.227), p < .001; B5 Neuroticism: r = .134, 95% CI
(.095, .173), p < .001. For the HEXACO, no study included both self- and other-ratings of workplace deviance.
8 When excluding two studies from this analysis that measured workplace deviance with other-reports (Kluemper, Mclarty, & Bing, 2014 Study 1; Richards & Schat, 2011), the explained variance in workplace deviance using the B5 domain scales slightly increases, k =
10, N = 4591, R2 = .191, 95% CI for R2 (.140; .253).
9 This provides further support for the fact that these facets belong to different domain scales, as is the case in the HEXACO: Anger is part of HEXACO Agreeableness, whereas
Anxiety is part of HEXACO Emotionality.
142
143 CHAPTER 5
CHAPTER 5 AGE AND WORKPLACE DEVIANCE: A META- ANALYSIS
This chapter is based on Pletzer, J. L., Oostrom, J. K., & Voelpel, S. C. (2017). Age and workplace deviance: A meta-analysis. Manuscript submitted for publication. Paper drafts have been presented at the WAOP Conference 2016 and at the AOM Conference 2017.
144 CHAPTER 5
Abstract
In this meta-analysis, we examine the relationship between age and workplace deviance. We find a small but significant negative correlation (r = -.088, k = 135). As hypothesized based on the socio-emotional selectivity theory and the neo-socioanalytical model of personality change, this relationship is (partially) mediated by personality (i.e., conscientiousness, agreeableness, and extraversion) and by negative affect. Age shows a similar correlation with the two subfacets of workplace deviance: interpersonal and organizational deviance. Several methodologically and practically relevant moderators are examined. For example, the negative correlation between age and workplace deviance is stronger when workplace deviance is measured through self-reports as compared to other-reports. Results of this meta- analysis suggest that hiring older individuals could benefit organizations because it might reduce levels of workplace deviance and thereby lead to a competitive advantage for these organizations. Methodological, theoretical, and practical implications, as well as limitations and future research ideas, are discussed.
Keywords: counterproductive work behavior, workplace deviance, personality, negative affect, socio-emotional selectivity theory, neo-socioanalytical model of personality change, job selection, hiring, age
145 AGE AND WORKPLACE DEVIANCE
Introduction
An important behavioral determinant of job performance is workplace deviance, which describes voluntary behaviors that harm the wellbeing of the organization and its employees (Robinson & Bennett, 1995). As such, levels of employee deviant behavior are often used in performance evaluations (Lievens, Conway, & De Corte, 2008; Welbourne,
Johnson, & Erez, 1998). Workplace deviance can have far-reaching and detrimental consequences for a number of important outcomes at work. For example, it decreases task performance (for a review, see Sackett, 2002) and just a few deviant employees may impair team performance (Dunlop & Lee, 2004). Furthermore, deviant behavior inflicts psychological harm on coworkers (Pearson, Andersson, & Porath, 2000) and thereby increases coworkers’ stress levels and may even lead to increased levels of depression and anxiety among victims (Cortina et al., 2001). Consequently, estimates of the annual costs of workplace deviance are tremendous, varying between $50 billion (Henle et al., 2005) and $6 to $200 billion in the USA alone (Robinson & Bennett, 1995), but the real costs might be even higher due to the hidden nature of these behaviors.
The most commonly used definition of workplace deviance describes it as “voluntary behavior that violates significant organizational norms and in so doing threatens the wellbeing of an organization, its members, or both” (Robinson & Bennett, 1995, p. 556). This definition distinguishes between two subfacets of workplace deviance: interpersonal and organizational deviance. The former describes deviant behavior directed toward other members of the organization, such as insulting a colleague or disobeying the supervisor’s instructions. The latter characterizes deviant behavior targeting the organization, such as coming late to work or stealing from the employer. Both forms can vary in severity, but are always detrimental and costly for organizations (Henle et al., 2005; Sackett, 2002). The term counterproductive work behavior is often used as a synonym for workplace deviance.
Previous research has shown that workplace deviance can be caused by characteristics of the organizational environment (e.g., abusive supervision; Mitchell & Ambrose, 2007) or
146 CHAPTER 5 by stable individual differences (e.g., personality; Hastings & O’Neill, 2009). One important category of such stable individual differences are demographic characteristics. For example,
Ng, Lam, and Feldman (2016) recently provided meta-analytic evidence that, on average, women behave in a slightly less deviant manner in the workplace than men. Another important demographic characteristic is age. Previous meta-analyses have only indirectly addressed the relationship between age and workplace deviance. Berry, Carpenter, and Barratt
(2012), who meta-analytically examined the incremental validity of other-reports over and above self-reports of workplace deviance, reported a small negative correlation between age and workplace deviance (r = -.05, k = 13). Ng and Feldman (2008) focused on the relationship between age and various conceptualizations of job performance, including workplace deviance. They found a negative correlation between age and self-rated (r = -.12, k = 28) and other-rated workplace deviance (r = -.09, k = 6). Lastly, Berry, Ones, and Sackett (2007), who examined various predictors of workplace deviance, showed that age correlates negatively with interpersonal (r = -.05, k = 14) and organizational workplace deviance (r = -.09, k = 12).
Even though these previous meta-analyses investigated age as one possible predictor of workplace deviance, they were based on a small number of studies, did not provide theoretical arguments for the effect, rarely distinguished between interpersonal and organizational workplace deviance, nor examined other important moderators. The current meta-analysis therefore extends existing literature by offering a comprehensive quantitative overview of the age-workplace deviance relationship, by testing theory-driven hypotheses for this relationship, by distinguishing between different subtypes and rater sources, and by examining several theoretically and practically relevant moderators (e.g., type of questionnaire, country characteristics).
Socio-Emotional Selectivity Theory and Neo-Socioanalytical Model of Personality
Change
The age-workplace deviance relationship can be explained with the socio-emotional selectivity theory (Carstensen, 1992) and with the neo-socioanalytical model of personality
147 AGE AND WORKPLACE DEVIANCE change (Roberts & Wood, 2006). An important determinant of workplace deviance is negative affect (Bing et al., 2007; Dalal, 2005; Lee & Allen, 2002; Spector & Fox, 2002).
Almost all deviant behaviors originate out of some form of frustration, anger, or aggravation
(Fox & Spector, 1999). Research has consistently found that individuals develop a more pronounced preference for positive over negative emotions with increasing age (i.e., positivity effect; e.g., Mather & Carstensen, 2005). This finding can be explained by the socio- emotional selectivity theory, which states that as individuals grow older and their time horizons shrink, they become increasingly selective and spend more time on emotionally meaningful goals and activities (Carstensen, 1992). Hence, older individuals are motivated to retain positive memories and to self-select into positive and meaningful situations. They also experience fewer interpersonal conflicts and less stress in response to conflicts if they occur
(Birditt, Fingerman, & Almeida, 2005). In addition to this increased motivation to avoid negative emotions and conflicts, older (rather than younger) individuals have also been found to use more appropriate emotion regulation strategies due to their increased experience with emotional situations (Charles, 2010; Scheibe & Carstensen, 2010; Scheibe, Sheppes, &
Staudinger, 2015). These emotion regulation skills decrease the likelihood of experiencing negative emotions even further (Mather & Carstensen, 2005). Thus, according to socio- emotional selectivity theory and research findings that build on it, we expect that negative affect decreases with increasing age, which is subsequently associated with a decrease in levels of workplace deviance.
Another reliable predictor of workplace deviance are personality characteristics (Berry et al., 2007). The neo-socioanalytical model of personality change posits that personality characteristics, such as those in the five-factor model (Digman, 1990), change across the adult lifespan (Roberts & Wood, 2006). Longitudinal and cross-sectional studies have shown that
Agreeableness, Conscientiousness, and Emotional Stability (versus Neuroticism) increase with age (Roberts & Mroczek, 2008; Roberts, Walton, & Viechtbauer, 2006), and meta- analytic evidence indicates that these exact same personality characteristics are negatively
148 CHAPTER 5 correlated with workplace deviance (r = -.23 to -.35; Berry et al., 2007). The other two personality domain scales, Openness to Experience and Extraversion, have not been shown to significantly correlate with age or workplace deviance. Thus, according to the neo- socioanalytical model of personality change and research linking personality to levels of workplace deviance, we also expect that personality changes with increasing age, which is subsequently associated with a change in levels of workplace deviance.
Based on these two theories, we hypothesize the following:
Hypothesis 1: Age correlates negatively with workplace deviance.
Hypothesis 2: Negative affect mediates the negative relationship between age and
workplace deviance.
Hypothesis 3: The personality domain scales of Conscientiousness, Agreeableness,
and Neuroticism mediate the negative relationship between age and workplace
deviance.
Moderators of the Age – Workplace Deviance Relationship
The relationship between age and workplace deviance is likely to be influenced by certain methodological or demographic characteristics of the included studies. In the following, we will outline our expectations for a variety of theoretically and practically relevant moderators of this relationship.
Country characteristics: Pension coverage & social connections in old age. As described above, negative affect is an important predictor of workplace deviance (Bing et al.,
2007; Dalal, 2005; Lee & Allen, 2002; Spector & Fox, 2002). According to the socio- emotional selectivity theory, affective and emotional experiences become less negative with increasing age (Carstensen, 1992; Mather & Carstensen, 2005). Two important predictors of emotional experiences and wellbeing are financial security and social relationships (Bridges
& Disney, 2010; Grant, Christianson, & Price, 2007; Green & Leeves, 2013; Kok et al., 2013;
Miron-Shatz, 2009). Hence, we chose to investigate the moderating role of age-relevant conceptualizations of these two important determinants of emotional experiences and
149 AGE AND WORKPLACE DEVIANCE wellbeing on a country-level: a country’s pension coverage and the average number of social connections in old age.
An important protective factor against workplace deviance is job or income security
(e.g., Reisel, Probst, Chia, Maloles, & König, 2010; Tian, Zhang, & Zou, 2014). If employees feel that they have a future in their organization and do not fear being terminated, they are less likely to behave deviantly at work. While job security remains important for employees of all ages, older employees additionally place more value on a related construct, namely their financial security after retirement (Taylor & Shore, 1995). One indicator for financial security after retirement is a country’s pension coverage (i.e., the percentage of individuals receiving a pension after retirement). Hence, the relationship between age and workplace deviance should be affected by a country’s pension coverage. If older employees worry less about their financial security after retirement, they should be less likely to show workplace deviance. We therefore expect that the correlation between age and workplace deviance becomes more negative for countries with high pension coverage.
Similarly, social support functions as a buffer against negative experiences at work
(Viswesvaran, Sanchez, & Fisher, 1999). For example, employees who do not experience work-family conflict are less likely to engage in deviant workplace behavior (e.g., Darrat,
Amyx, & Bennett, 2010; Ferguson, Carlson, Hunter, & Whitten, 2012). Significant social connections should therefore also function as a protective factor against the occurrence of workplace deviance, because employees with a strong social support network comprising families, friends, and coworkers are less likely to react to the negative effects of stress at work
(Viswesvaran et al., 1999; Wills, 1985). Especially in old age, the number of significant social connections usually decreases (Bhattacharya, Ghosh, Monsivais, Dunbar, & Kaski, 2016).
Hence, we expect the average number of significant social connections of older individuals in a given country to moderate the relationship between workplace deviance and age, in a way that the effect size will be more negative in countries with high levels of significant social connections in old age.
150 CHAPTER 5
Gender. A recent meta-analysis showed that female employees behave less deviantly in the workplace than male employees (Ng et al., 2016). These authors also found that the relationship between gender and self-rated interpersonal workplace deviance became less negative in samples with a higher average age. While no explanation for their findings was provided, building on their results we would expect that the relationship between age and
(interpersonal) workplace deviance becomes more negative when the average percentage of women included is higher in a respective study.
Self- versus other-reports. Previous research suggests that other-reports of workplace deviance are significantly correlated with self-reports (Berry et al., 2012). In their meta- analysis, Ng and Feldman (2008) found overlapping confidence intervals for the correlation between age and self-reported and other-reported workplace deviance (self-reports: r = -.12,
95% CI [-0.15, -0.10]; other-reports: r = -.09, 95% CI [-0.17, -0.02]). As the number of studies included for other-reports was really low in their meta-analysis (k = 6), it is important to examine whether the source of the workplace deviance rating (self- versus other-report) moderates the relationship between workplace deviance and age. Results of this moderation analysis will demonstrate whether self- and other-reports of workplace deviance are differently susceptible to age differences in employees, which carries important implications for future studies of workplace deviance in age-diverse samples.
Workplace deviance form. In a similar vein, we will examine the extent to which age correlates differently with interpersonal and organizational workplace deviance. Meta- analytic evidence indicates that these two sub-dimensions correlate highly (r = .70; Dalal,
2005). In addition, a previous meta-analysis by Berry, Ones, and Sackett (2007) found no significant difference in the relationship of age with interpersonal (r = -.05) and organizational workplace deviance (r = -.09). However, they did not statistically test the difference between these correlation coefficients nor reported 95% confidence intervals.
Interpersonal and organizational workplace deviance show different relationships with personality dimensions (Berry et al., 2007) and might be more or less prevalent and
151 AGE AND WORKPLACE DEVIANCE destructive in different industries; for example, service industries with a lot of customer contact would probably suffer more from interpersonal workplace deviance than producing industries with no or low customer contact. For future studies of workplace deviance and for practitioners it is therefore important to examine if age is differently related to interpersonal or organizational workplace deviance.
Workplace deviance questionnaire. Workplace deviance can be assessed with a variety of questionnaires. To the best of our knowledge, no research has yet examined the questionnaire as a moderator of the age-workplace relationship. We will therefore exploratorily examine if the three most commonly used workplace deviance questionnaires
(Aquino, Lewis, & Bradfield, 1999; Bennett & Robinson, 2000; Spector et al., 2006) differ in their relationship with age. If these questionnaires are differently susceptible to age differences in respondents, researchers should be even more careful in selecting a workplace deviance questionnaire, especially when studying workplace deviance in age-diverse samples.
Curve of the age – workplace deviance relationship. Ng and Feldman (2008) showed in their meta-analysis that the relationship between age and workplace deviance follows a negative, concave curve when categorizing studies according to the average age in their sample (younger than 25: r = -.01; 25-39 years old: r = -.12, older than 40: r = -.17).
Because the number of included studies in each respective age category was low (k per category was not reported, but overall k = 28, suggesting that the average k was only around
9) we will try to replicate these results in a much larger sample.
Contribution of the Current Meta-Analysis
This meta-analysis makes several contributions to the literature. First, we provide a comprehensive meta-analytic overview of the age-workplace deviance relationship. Second, we will test two possible explanatory mechanisms based on the socio-emotional selectivity theory (Carstensen, 1992) and the neo-socioanalytical model of personality change (Roberts
& Wood, 2006). Third, we do not only provide meta-analytic results for the relationship between age and workplace deviance, but also for the relationships between age and negative
152 CHAPTER 5 affect, negative affect and workplace deviance, age and personality, and between personality and workplace deviance. Fourth, we test several theoretically, methodologically, and practically relevant moderators of the relationship between age and workplace deviance.
Method
Systematic Literature Search
We conducted a systematic literature search in several scientific databases, including
PsycInfo, PsycArticles, and Business Source Premier. We searched for articles containing the keywords workplace deviance or counterproductive work behavior in the abstract or title and the keyword age in the entire text. After duplicates were removed, we were able to identify
3535 scientific articles. We screened the title and the abstract of each of these articles to assess if the article included a measure of workplace deviance. This strategy generated 674 articles, which were examined in full. In addition, we searched GoogleScholar for more articles containing the abovementioned keywords. Finally, we examined prior meta-analyses published on the topic of workplace deviance (Berry et al., 2012, 2007; Ng & Feldman, 2008;
Ng et al., 2016) to see whether these contained any additional studies we might have missed in our literature search.
Several criteria had to be met for a study to be included in our meta-analysis. First, the article had to report the correlation coefficient (r) between workplace deviance and age, and the respective sample size. Second, the article had to report data from field studies and not from experimental studies. Third, workplace deviance had to be measured on an individual level. Studies that reported levels of workplace deviance on a team or organizational level were excluded. Fourth, age had to be measured on a continuous scale. Studies that used a categorical measure of age were excluded. Note that some studies measured workplace deviance at two points in time. Because time 2 data could potentially be confounded by the time lag between measurement points, we only coded time 1 data. Based on these inclusion criteria, 109 scientific articles containing 135 individual studies and 205 effect sizes were included in the meta-analysis. The articles we were able to find were published between 1990
153 AGE AND WORKPLACE DEVIANCE and 2016, with a median publication year of 2011. On average, each individually coded effect size was based on a sample of 303 participants. The first author and a trained student assistant coded all effect sizes and study characteristics independently from each other, which resulted in more than 90% agreement. Any inconsistencies in the codings were resolved after revisiting the article and discussing the respective coding. The codings for each included effect size are in Table 1.
154 CHAPTER 5
Table 1 Studies, Effect Sizes, and Codings included in the Meta-Analysis Study Form r N Questionnaire Rater Country PC SC %F Alias et al. (2013) ID -.03 429 B&R SR MY ------64.6 OD .00 429 B&R SR MY ------64.6 Andreoli & Lefkowitz (2009) WD -.02 145 --- SR USA 92.5 94.0 48.3 Aquino & Douglas (2003) ID -.08 308 D&M SR USA 92.5 94.0 44.2 Aquino et al. (2004) ID -.04 192 B&R SR USA 92.5 94.0 34.0 OD -.04 192 B&R SR USA 92.5 94.0 34.0 Banks et al. (2012) ID .02 108 B&R SR KR 77.6 60.0 40.0 OD .06 108 B&R SR KR 77.6 60.0 40.0 Bolton et al. (2012) WD -.16 175 Spector SR USA 92.5 94.0 51.0 Bordia et al. (2008) S1 WD -.01 153 CR OB PH 28.3 76.0 68.0 Bordia et al. (2008) S2 OD -.02 168 Aquino OR PH 28.3 76.0 48.2 Bordia et al. (2008) S3 ID -.10 187 Aquino OR PH 28.3 76.0 58.0 OD .00 187 Aquino OR PH 28.3 76.0 58.0 Bowling (2010) OD -.25 209 B&R SR USA 92.5 94.0 56.0 Bowling et al. (2010) OD -.24 227 B&R SR USA 92.5 94.0 59.0 Bowling et al. (2011) S1 ID -.02 193 B&R SR USA 92.5 94.0 64.0 OD -.04 193 B&R SR USA 92.5 94.0 64.0 Bowling et al. (2011) S2 ID -.21 220 B&R SR USA 92.5 94.0 57.0 OD -.24 220 B&R SR USA 92.5 94.0 57.0 Bowling et al. (2011) S3 ID -.14 122 B&R SR USA 92.5 94.0 77.0 OD -.13 122 B&R SR USA 92.5 94.0 77.0 Bruk-Lee & Spector (2006) ID -.22 121 Fox SR USA 92.5 94.0 78.0 OD -.27 121 Fox SR USA 92.5 94.0 78.0 ID -.20 121 Fox OR USA 92.5 94.0 78.0 OD -.15 121 Fox OR USA 92.5 94.0 78.0 Chao et al. (2011) WD -.19 131 G&S SR CN 74.4 63.0 58.0 Chen et al. (2013) WD -.17 310 B&R SR TW ------59.4 Chirumbolo (2015) WD .09 203 K&L SR IT 81.0 91.0 53.7 Chiu & Peng (2008) ID -.07 233 B&R SR TW ------70.0 OD -.11 233 B&R SR TW ------70.0 Chullen et al. (2010) ID -.07 1924 CR OB USA 92.5 94.0 86.0 OD -.09 1924 CR OB USA 92.5 94.0 86.0 Cohen et al. (2013) WD -.27 411 Spector SR USA 92.5 94.0 55.6 Connelly et al. (2011) ID -.19 157 --- SR CA 97.7 94.0 54.0 OD -.16 157 --- SR CA 97.7 94.0 54.0 Côté et al. (2011) ID -.18 246 B&R SR CA 97.7 94.0 73.0 Cronin & Smith (2011) WD -.05 161 --- SR USA 92.5 94.0 56.0 Dahling et al. (2008) WD -.07 323 F&S OR USA 92.5 94.0 68.5 Dahling et al. (2012) ID .00 211 F&S SR USA 92.5 94.0 71.6 OD -.03 211 F&S SR USA 92.5 94.0 71.6 WD -.03 211 F&S SR USA 92.5 94.0 71.6 De Clercq et al. (2014) OD -.21 272 B&R SR UA 95.0 81.0 46.0 de Vries & van Gelder (2015) WD -.08 455 Self SR NL 100.0 81.0 45.3 Deckop et al. (2014) ID -.04 270 B&R OR USA 92.5 94.0 50.0 OD -.16 270 B&R OR USA 92.5 94.0 50.0 Devonish & Greenidge (2010) ID -.02 211 Spector SR BB ------54.5 OD -.01 211 Spector SR BB ------54.5 Dubbelt et al. (2014) ID -.10 285 B&R SR NL 100.0 81.0 40.7 OD .01 285 B&R SR NL 100.0 81.0 40.7 ID .27 64 Stewart OR NL 100.0 81.0 40.7 OD .13 64 Stewart OR NL 100.0 81.0 40.7 Duffy et al. (1998) WD -.10 181 Self-made SR USA 92.5 94.0 11.0 Duffy et al. (2006) WD -.03 737 Self SR SI 95.1 88.0 7.0 El Akremi et al. (2010) ID -.06 602 B&R SR FR 100.0 93.0 57.0 OD -.14 602 B&R SR FR 100.0 93.0 57.0 Enns & Rotundo (2012) WD -.09 110 Fox SR USA 92.5 94.0 66.7 Erkutlu & Chafra (2013) OD .07 848 B&R OR TR 88.1 81.0 32.0 Eschleman et al. (2014) ID -.15 268 M&A SR USA 92.5 94.0 50.0 OD -.20 268 B&R SR USA 92.5 94.0 50.0 Ferris et al. (2009) OD -.17 230 Aquino SR USA 92.5 94.0 47.0 Ferris et al. (2009b) WD -.22 123 B&R SR ------66.0 Fine et al. (2010) WD .04 429 Self SR IL 73.6 91.0 62.0 Galperin, (2012) ID -.23 240 B&R SR CA 97.7 94.0 34.0 OD -.13 240 B&R SR CA 97.7 94.0 34.0 Gill et al. (2011) WD .08 120 B&R OR KR 77.6 60.0 47.0 Gottfredson & Holland (1990) WD -.36 71 Self SR USA 92.5 94.0 75.0 Gruys et al. (2010) S1 WD -.24 317 Self SR USA 92.5 94.0 56.0 Gruys et al. (2010) S2 ID .02 262 B&R OR USA 92.5 94.0 --- OD .14 262 B&R OR USA 92.5 94.0 --- Gutworth & Dahling (2013) OD -.07 147 B&R SR USA 92.5 94.0 73.5 Harvey et al. (2014) OD .12 396 Aquino SR USA 92.5 94.0 56.0 Harvey, et al. (2014b) OD .05 152 Aquino OR USA 92.5 94.0 66.0 Hastings & Finegan (2011) ID -.07 200 B&R SR CA 97.7 94.0 67.5 OD -.08 200 B&R SR CA 97.7 94.0 67.5 Henle (2005) WD -.16 151 B&R SR USA 92.5 94.0 53.6 Holtz & Harold (2013) S1 ID -.20 318 B&R SR USA 92.5 94.0 53.0
155 AGE AND WORKPLACE DEVIANCE
OD -.21 318 B&R SR USA 92.5 94.0 53.0 WD -.23 318 B&R SR USA 92.5 94.0 53.0 Holtz & Harold (2013) S2 ID -.17 122 B&R OR USA 92.5 94.0 65.0 OD -.26 122 B&R OR USA 92.5 94.0 65.0 WD -.23 122 B&R OR USA 92.5 94.0 65.0 Holtz & Harold (2013b) S1 WD .11 158 Dalal OR USA 92.5 94.0 61.0 Holtz & Harold (2013b) S2 WD -.08 105 Dalal OR USA 92.5 94.0 31.0 Hunter & Penney (2014) ID .03 438 Spector SR USA 92.5 94.0 62.0 OD -.14 438 Spector SR USA 92.5 94.0 62.0 CDD .01 438 Self SR USA 92.5 94.0 62.0 Iliescu et al. (2015) S1 WD .02 226 Spector SR RO 98.0 75.0 49.0 WD .06 226 Spector OR RO 98.0 75.0 49.0 Iliescu et al. (2015) S2 WD -.02 245 B&R SR ------47.0 WD .05 245 B&R OR ------47.0 Jensen & Patel (2011) ID .02 517 C&G SR EU ------47.0 OD .04 517 C&G SR EU ------47.0 Jones (2009) ID -.08 424 B&R & S&F SR CA 97.7 94.0 79.5 OD -.13 424 B&R & S&F SR CA 97.7 94.0 79.5 Khan et al. (2014) WD .03 140 CC&M SR PK 2.3 60.0 45.0 Kluemper et al. (2013) S1 ID -.11 220 B&R SR USA 92.5 94.0 55.0 OD -.22 220 W&A OR USA 92.5 94.0 55.0 Kluemper et al. (2013) S2 ID -.08 100 W&A OR USA 92.5 94.0 49.0 Kwok et al. (2005) WD .02 155 Self-made SR HK ------70.0 Lee & Allen (2002) WD .00 149 B&R SR CA 97.7 94.0 95.4 Lian et al. (2012) S1 ID -.17 264 B&R SR ------54.0 Lian et al. (2012) S2 ID -.06 171 Stewart OR ------52.0 Lian et al. (2012) S3 ID -.08 198 B&R SR ------55.0 Liao et al. (2004) ID -.20 286 B&R SR USA 92.5 94.0 67.0 OD -.17 286 B&R SR USA 92.5 94.0 67.0 Little et al. (2011) OD -.19 331 H&C OR USA 92.5 94.0 37.0 Liu & Ding (2014) ID -.27 460 B&R SR TW ------47.6 OD -.23 460 B&R SR TW ------47.6 Liu et al. (2010) S1 ID -.18 283 M&A OR CN 74.4 63.0 32.9 Liu et al. (2010) S2 ID .07 222 M&A SR CN 74.4 63.0 50.0 ID .03 222 M&A OR CN 74.4 63.0 50.0 Mackey et al. (2015) S1 ID -.22 96 Aquino SR USA 92.5 94.0 56.0 SDD -.13 96 Aquino SR USA 92.5 94.0 56.0 Mackey et al. (2015) S2 ID .03 130 Aquino SR USA 92.5 94.0 65.0 SDD .11 130 Aquino SR USA 92.5 94.0 65.0 Marasi et al. (2016) WD -.08 353 B&R SR USA 92.5 94.0 92.0 Mawritz et al. (2014) OD -.04 221 B&R OR USA 92.5 94.0 55.0 Mayer et al. (2012) S1 OD -.26 367 B&R SR USA 92.5 94.0 46.3 Mayer et al. (2012) S4 ID .20 218 B&R SR USA 92.5 94.0 53.0 OD -.20 218 B&R SR USA 92.5 94.0 53.0 Mitchell & Ambrose (2007) ID -.17 427 B&R SR USA 92.5 94.0 56.9 OD -.28 427 B&R SR USA 92.5 94.0 56.9 SDD -.16 427 B&R SR USA 92.5 94.0 56.9 Mulki et al. (2006) OD -.11 208 B&R SR USA 92.5 94.0 68.8 Neves & Champion (2015) ID -.02 518 B&R & Aquino OR ------51.0 OD .01 518 B&R & Aquino OR ------51.0 Neves & Story (2015) OD -.04 224 Aquino OR ------46.0 Norman et al. (2010) WD -.22 199 F&S SR USA 92.5 94.0 47.2 Penhaligon et al. (2013) OD -.01 189 B&R SR AU 83.0 92.0 56.1 Penney et al. (2011) WD -.15 239 Spector SR USA 92.5 94.0 55.5 Pitariu & Budean (2015) ID -.44 281 B&R SR RO 98.0 75.0 86.1 OD -.34 281 B&R SR RO 98.0 75.0 86.1 Probst et al. (2007) OD -.20 144 B&R SR USA 92.5 94.0 65.0 Resick et al. (2013) WD -.15 190 B&R OR USA 92.5 94.0 46.0 Restubog et al. (2007) ID -.18 162 Aquino SR PH 28.3 76.0 53.7 OD -.23 162 Aquino SR PH 28.3 76.0 53.7 Restubog et al. (2010) WD -.10 125 CR OB PH 28.3 76.0 59.0 Restubog et al. (2013) OD -.02 168 Aquino OR PH 28.3 76.0 48.2 Restubog et al. (2015) S1 WD .21 146 Self OR AU 83.0 92.0 52.0 Restubog et al. (2015) S2 WD .00 168 Self OR PH 28.3 76.0 42.3 Rosen & Levy (2013) WD -.09 285 B&R SR USA 92.5 94.0 71.0 Sackett et al. (2006) ID -.03 805 B&R SR USA 92.5 94.0 75.9 OD -.09 805 B&R SR USA 92.5 94.0 75.9 WD -.08 805 B&R SR USA 92.5 94.0 75.9 Sakurai (2011) WD -.27 202 B&R SR USA 92.5 94.0 44.3 Salami (2010) WD .20 422 Spector SR NG 5.0 74.0 --- Samnani et al. (2013) OD -.16 221 B&R SR CA 97.7 94.0 72.0 Semmer et al. (2010) S1 ID -.12 199 B&A SR CH 100.0 91.0 57.0 SDD -.07 199 B&A SR CH 100.0 91.0 57.0 Semmer et al. (2010) S2 ID -.14 205 B&R SR CH 100.0 91.0 55.6 OD -.36 205 B&R SR CH 100.0 91.0 55.6 Shao et al. (2011) ID -.23 490 B&R SR USA 92.5 94.0 71.0 Sharkawi et al. (2013) ID .18 192 S&F SR MY ------15.6 OD .08 192 S&F SR MY ------15.6 Shoss et al. (2013) S1 WD .06 148 Spector OR PH 28.3 76.0 48.6 Shoss et al. (2013) S2 WD -.09 254 Aquino SR PH 28.3 76.0 63.0
156 CHAPTER 5
Shoss et al. (2013) S3 OD -.03 187 Aquino OR PH 28.3 76.0 55.1 Smoktunowicz et al. (2015) WD -.04 607 Spector SR PL 96.5 87.0 20.0 Spector & Zhou (2014) ID -.03 915 Spector SR USA 92.5 94.0 78.0 OD -.02 915 Spector SR USA 92.5 94.0 78.0 Sprung (2011) ID -.25 208 Spector SR USA 92.5 94.0 46.4 OD -.28 208 Spector SR USA 92.5 94.0 46.4 WD -.27 208 Spector SR USA 92.5 94.0 46.4 Stamper & Masterson (2002) OD -.06 257 B&R OR USA 92.5 94.0 75.0 Stouten et al. (2013) S2 OD -.14 410 B&R SR NL 100.0 91.0 36.2 Stouten et al. (2013) S3 ID -.07 168 M&A OR USA 92.5 94.0 52.0 OD -.10 168 B&R OR USA 92.5 94.0 52.0 Sulea et al. (2012) WD -.02 258 Spector SR RO 98.0 75.0 52.0 Tepper et al. (2009) S1 ID -.19 797 S&F SR USA 92.5 94.0 53.7 OD -.31 797 S&F SR USA 92.5 94.0 53.7 Tepper et al. (2009) S2 ID -.14 356 B&R & S&F SR USA 92.5 94.0 65.0 OD -.31 356 B&R & S&F SR USA 92.5 94.0 65.0 Thau & Mitchell (2010) S1 ID .37 216 M&A SR USA 92.5 94.0 57.4 OD -.13 216 B&R SR USA 92.5 94.0 57.4 Thau & Mitchell (2010) S2 ID -.15 365 M&A SR USA 92.5 94.0 68.0 OD -.25 365 B&R SR USA 92.5 94.0 68.0 Thau et al. (2007) ID .01 129 B&R OR NL 100.0 91.0 88.0 Thau et al. (2007b) S1 WD -.06 306 B&R SR USA 92.5 94.0 42.0 Thau et al. (2007b) S2 WD .00 87 Kickul OR DE 100.0 90.0 40.0 Thau et al. (2007b) S3 WD -.03 106 B&R OR NL 100.0 91.0 40.2 Thau et al. (2009) S1 ID -.13 373 B&R SR USA 92.5 94.0 42.0 OD -.11 373 B&R SR USA 92.5 94.0 42.0 Thau et al. (2009) S2 ID -.21 1477 M&A SR USA 92.5 94.0 50.0 OD -.21 1477 B&R SR USA 92.5 94.0 50.0 Tian et al. (2014) WD -.13 366 B&R SR CN 74.4 63.0 52.5 Van den Broeck et al. (2014) ID -.18 451 B&R SR RO 98.0 75.0 58.0 OD -.16 451 B&R SR RO 98.0 75.0 58.0 Wang et al. (2012) ID -.11 283 B&R SR CN 74.4 63.0 32.9 OD -.12 283 B&R SR CN 74.4 63.0 32.9 SDD -.18 283 B&R OR CN 74.4 63.0 32.9 Winkel et al. (2011) ID .00 234 B&R SR USA 92.5 94.0 56.0 OD -.09 234 B&R SR USA 92.5 94.0 56.0 Wu et al. (2014) ID -.05 233 B&R SR CN 74.4 63.0 25.8 Yang & Diefendorff (2009) ID -.04 231 Self SR HK ------70.0 OD -.17 231 Self SR HK ------70.0 Yang (2008) ID -.06 256 B&R SR USA 92.5 94.0 62.0 OD -.10 256 B&R SR USA 92.5 94.0 62.0 Yang et al. (2013) ID .03 361 B&R SR CN 74.4 63.0 85.0 OD .04 361 B&R SR CN 74.4 63.0 85.0 Zagenczyk et al. (2014) S2 OD -.04 156 Aquino OR PH 28.3 76.0 51.0 Zagenczyk et al. (2014) S3 OD -.05 152 Aquino OR PH 28.3 76.0 56.6 Zagenczyk et al. (2014) S4 OD .01 259 Aquino OR PH 28.3 76.0 64.9 Zhao et al. (2013) ID .01 239 Y&D SR CN 74.4 63.0 36.0 OD -.11 239 Y&D SR CN 74.4 63.0 36.0 Zoghbi-Manrique-de-Lara (2011) OD .03 270 B&R SR ES 68.2 89.0 35.4 Note. Abbreviations: ID = Interpersonal workplace deviance, OD = organizational workplace deviance, WD = overall workplace deviance, CDD = customer-directed workplace deviance, SDD = supervisor-directed deviance; B&R = Bennett & Robinson (2000), D&M = Douglas & Martinko (2001), Spector = Spector et al. (2006), CR = Company Records, Aquino = Aquino et al. (1999), Fox = Fox et al. (2001), G&S = Gruys & Sackett (2003), K&L = elloway & Loughlin (2002), F&S = Fox & Spector (1999), Stewart = Stewart et al. (2009), M&A = Mitchell & Ambrose (2007), Dalal = Dalal et al. (2009), C&G = Coyne & Gentile (2006), S&F = Skarlicki & Folger (1997), CC&M = Cohen-Charash & Mueller (2007), W&A = Williams & Anderson (1991), H&C = Hollinger & Clark (1982), B&A = Blau & Andersson (2005), S&F = Spector & Fox (2002), Kickul = Kickul et al. (2001), Y&D = Yang & Diefendorff (2009), Self = The questionnaire was either developed by the authors themselves or included items from different questionnaires; SR = self-rated WD, OR = supervisor- or coworker-rated WD, OB = WD rated by objective company records; PC = Pension Coverage, SC = Social Connections, %F = % Female, AU = Australia, BB = Barbados, CA = Canada, CN = China, DE = Germany, ES = Spain, EU = Europe, FR = France, HK = Hong Kong, IL = Israel, IT = Italy, KR = South Korea, MY = Malaysia, NG = Nigeria, NL =The Netherlands, PH = Philippines, PK = Pakistan, PL = Poland, RO = Romania, SI = Slovenia, TR = Turkey, TW = Taiwan, UA = Ukraine
157 AGE AND WORKPLACE DEVIANCE
Definition of Variables
Age. The average age of participants in the individual studies ranged between 18.85 and 49.00 years, with an average age of 34.51 years (SD = 7.13).
Workplace deviance. Workplace deviance can be assessed as an overall construct which encompasses all deviant behaviors (k = 49). However, many articles differentiate between interpersonal and organizational deviance. Interpersonal workplace deviance includes all behaviors directed at other individuals in the organization (k = 66). Examples of such behaviors are insulting a coworker or being rude to customers. Some studies measured other forms of interpersonal workplace deviance, such as customer-directed (k = 1) or supervisor-directed deviance (k = 5). These studies were only included in the overall analysis, but not in the moderator analyses. Organizational workplace deviance includes all deviant behaviors directed at the organization in which an individual is employed (k = 77). Examples of organizational workplace deviance include stealing from the organization or not following the rules. If a study assessed only one very specific behavior, such as absenteeism, the effect size was not included.
Moderator variables
We coded several study characteristics that vary across the studies included in the meta-analysis. The study characteristic we coded and the number of studies with coded effect sizes at each level of the coded variable are described below (see Table 1 for the codings)
Workplace deviance measure. Workplace deviance can be assessed with a variety of questionnaires. Of the 135 individual studies included in this meta-analysis, most of them used Bennett and Robinson’s (2000) questionnaire (k = 65), followed by the questionnaires by
Aquino et al. (1999, k = 15) and Spector et al. (2006, k = 12). The remaining studies used other questionnaires, a combination of questionnaires, or a self-made questionnaire (k = 40).
Three studies did not mention the questionnaire that was used.
Source. Several rating sources of workplace deviance have been used in the literature.
Most commonly, self-report measures are used (k = 96), followed by supervisor or coworker
158 CHAPTER 5 ratings (k = 39), even though this procedure is questionable because employees who behave deviantly usually try to hide such behavior from others (Bennett & Robinson, 2000; Spector,
1994). In rare cases, workplace deviance is assessed with objective company records (k = 3).
A few studies included both self-ratings and other-ratings (i.e., Bruk-Lee & Spector, 2006;
Dubbelt, Oostrom, Hiemstra, & Modderman, 2014; Iliescu, Ispas, Sulea, & Ilie, 2015; Liu,
Kwong Kwan, Wu, & Wu, 2010). In those cases, we used the self-rating in the overall analyses and included the other-rating in the respective moderator analysis. However, we also report results including other-ratings instead of self-ratings of workplace deviance.
Country characteristics. Research on workplace deviance has been conducted in a wide variety of countries. Most studies were conducted in the USA (k = 65), followed by the
Philippines (k = 13), Canada (k = 7), and China (k = 7). Other countries included in this sample are Australia, Barbados, France, Germany, Hong Kong, Israel, Italy, Malaysia,
Nigeria, Pakistan, Poland, Romania, Slovenia, South Korea, Spain, Switzerland, Taiwan,
Turkey, and Ukraine. For each country, we coded two variables taken from the Global Age
Watch Index 2015, which is based on data from the United Nations Department of Economic and Social Affairs, the World Bank, the World Health Organization, International
Labor Organizations, the UNESCO, and the Gallup World Poll (HelpAge International,
2015). We coded the pension coverage for each country, which is defined as the percentage of individuals over 65 years old who receive a pension (HelpAge International, 2015). In addition, we coded the average number of social connections in old age, defined as the percentage of individuals aged 50 or older who have friends or relatives they can count on when they experience problems (HelpAge International, 2015).
Gender. A recent meta-analysis (Ng et al., 2016) showed that male employees show higher levels of workplace deviance than female employees. Therefore, when available, we coded the percentage of female employees in the sample. Across all 134 studies which mentioned the percentage of female employees in their sample, it ranged between 7% and
95%, with an average of 56%.
159 AGE AND WORKPLACE DEVIANCE
Data Analysis
We used the Pearson product moment correlation coefficient (r) as a measure of effect size. All computations for this meta-analysis were conducted using Comprehensive Meta-
Analysis Software (CMA; Biostat, USA). Based on the assumption that we did not sample all studies from the population of studies and that heterogeneity was present in the sample of effect sizes, we used a random effects model with inverse-variance weights (Borenstein et al.,
2009). For our specific analysis, CMA performs the following steps:
1. For each study, the effect size data (r and N) were reported. Because the variance
in r is biased based on the magnitude of r, r is converted to Fisher’s z. CMA
computes all analyses on Fisher’s z and converts the final result back to r.
2. Each Fisher’s z value was weighted according to the inverse-variance method
(inverse of the sum of within- and between-study variance; DerSimonian & Laird,
1986).
3. A random-effects model was used to compute the overall mean weighted effect
size of all studies. The overall r is the sum of the product of all r (expressed as
Fisher’s z) and weights divided by the sum of all weights.
To assess heterogeneity between effect sizes, we computed a Q statistic and an I2 index, where I2 = 100%x(Q-df)/Q with df = k-1 and k = number of effect sizes (Borenstein et al., 2009). The I2 index indicates variability in the effect size based on real (rather than chance) differences between effect sizes. Benchmark values for the interpretation of I2 are:
25% = low, 50% = medium, and 75% = high (Higgins, Thompson, Deeks, & Altman, 2003b).
In the overall analysis, we combine the effect sizes for overall, interpersonal, and organizational workplace deviance if a study measures at least two of those to guarantee independence of effect sizes.
Publication Bias Analysis
In scientific research, studies with statistically significant findings are more likely to be published than studies with non-significant findings (Borenstein et al., 2009). Such
160 CHAPTER 5 publication bias can inflate the overall results of a meta-analysis. Hence, we tested for publication bias using Begg and Mazumdar’s (1994) rank correlation and Egger’s regression intercept (Egger et al., 1997). These indicators assess if effect sizes and precision differ systematically. If that is the case, publication bias is present in the data. Note that publication bias is unlikely to exist because the majority of studies included in this meta-analysis were not carried out to explicitly examine the relationship between age and workplace deviance.
Sensitivity and Moderator Analyses
The stability of the effect sizes over time was assessed by adding one study at a time to all previous studies (cumulative analysis). This allowed us to examine a trend over time.
The influence of individual studies on the overall effect size was assessed by removing one study at a time from the overall analysis (one-study removed analysis). We used a mixed- effects model to test the influence of moderating factors on the overall effect size (subgroup analyses and meta-regressions). We opted against the use of a fixed-effects model because we did not assume to have sampled all studies from the population of studies, and because random and systematic variation in the effect size distribution can be assumed to be present.
A limitation of this approach is that random- and mixed-effects models tend to be conservative and have an increased chance of Type II Errors (Lipsey & Wilson, 2001).
Two-Stage Meta-Analytical Structural Equation Modeling
A two-stage random-effects meta-analytical structural equation modeling (MASEM)
(Cheung, 2014, 2015) was conducted to test the mediation hypotheses (Hypotheses 2 and 3).
To do so, we additionally examined all included studies to identify whether a measure of either one of the Big Five personality domain scales (i.e., Openness to Experience,
Conscientiousness, Extraversion, Agreeableness, Neuroticism), or of negative affect was included. We coded the effect sizes between age, workplace deviance (overall, interpersonal, organizational), and the respective mediator (see Tables 2 through 7 for the codings).
MASEM combines meta-analysis with structural equation modeling and consists of two stages. In the first stage, the correlations between all the variables from all the primary
161 AGE AND WORKPLACE DEVIANCE studies are synthesized into an overall correlation matrix weighted by sample size. Whenever a study only reported correlations between interpersonal or organizational workplace deviance
(and not for overall workplace deviance) and the mediator or age, we averaged the correlations for interpersonal and organizational workplace deviance to a correlation for overall workplace deviance. In the second stage, this meta-analytic correlation matrix is subjected to a structural equation model to test the mediations. In the current study, we test three outcome variables (overall, interpersonal, and organizational workplace deviance) and six mediators (five personality domain scales and negative effect), which result in 18 tested mediations. If the indirect effect is significant and the direct effect decreases in magnitude, or becomes non-significant, a mediation is present. These analyses were conducted in R using the metaSEM package (Cheung, 2014).
162 CHAPTER 5
Table 2 Studies, Effect Sizes, and Codings included in the MASEM for Negative Affect r Study Age - Age - Age - Age - NA - NA - NA - N NA WD ID OD WD ID OD Alias et al. (2013) -.01 -.02 -.03 .00 -.28 -.30 -.25 429 Bowling et al. (2011) .01 -.03 -.02 -.04 .44 .41 .46 193 S1 Bowling et al. (2011) -.11 -.23 -.21 -.24 .41 .40 .41 220 S2 Chen et al. (2013) -.07 -.17 ------.59 ------310 Cohen et al. (2013) -.23 -.27 ------.47 ------411 Duffy et al. (2006) -.08 -.03 ------.10 ------737 Harvey et al. (2014) -.16 ------.12 ------.01 396 Holtz & Harold (2013) -.11 -.23 -.20 -.21 .31 .24 .31 318 S1 Holtz & Harold (2013) -.18 -.23 -.17 -.26 .15 .04 .22 122 S2 Khan et al. (2014) -.04 .03 ------.21 ------140 Lee & Allen (2002) -.06 .00 ------.14 ------149 Lian et al. (2012) S1 -.05 --- -.17 ------.30 --- 264 Lian et al. (2012) S2 -.25 --- -.06 ------.34 --- 171 Lian et al. (2012) S3 -.27 --- -.08 ------.50 --- 198 Liu & Ding (2014) -.07 -.25 -.27 -.23 .13 .12 .14 460 Liu et al. (2010) S1 -.06 --- -.18 ------.15 --- 283 Mayer et al. (2012) S4 -.08 -.20 -.20 -.20 .08 .04 .12 218 Sakurai (2011) -.19 -.27 ------.14 ------202 Salami (2010) -.15 .20 ------.34 ------422 Sprung (2011) -.22 -.27 -.25 -.28 .61 .61 .60 208 Tepper et al. (2009) S1 -.04 -.25 -.19 -.31 .23 .23 .23 797 Thau et al. (2009) S1 -.08 -.12 -.13 -.11 .23 .22 .24 373 Thau et al. (2009) S2 -.26 -.21 -.21 -.21 .50 .51 .48 1477 Wang et al. (2012) -.06 -.12 -.11 -.12 .15 .18 .11 283 Yang & Diefendorff -.14 -.11 -.04 -.17 .181 .17 .19 231 (2009) Note. NA = Negative Affect, WD = Workplace deviance, ID = interpersonal workplace deviance, OD = organizational workplace deviance.
163 AGE AND WORKPLACE DEVIANCE
Table 3 Studies, Effect Sizes, and Codings included in the MASEM for Conscientiousness r Study Age - Age - Age - Age - C - C - C - N C WD ID OD WD ID OD Alias et al. (2013) -.01 -.02 -.03 .00 -.26 -.13 -.36 429 Bowling (2010) .17 ------.25 ------.35 209 Bowling et al. (2010) .20 ------.24 ------.35 227 Bowling et al. (2011) .09 -.03 -.02 -.04 -.43 -.37 -.48 193 S1 Bowling et al. (2011) .17 -.23 -.21 -.24 -.31 -.28 -.33 220 S2 Dahling et al. (2012) -.01 -.03 .00 -.03 -.29 -.22 -.30 211 Ferris et al. (2009) .13 ------.17 ------.25 230 Iliescu et al. (2015) S2 -.02 -.02 ------.25 ------245 Jensen & Patel (2011) .01 .03 .02 .04 -.27 -.41 -.12 517 Kluemper et al. (2011) .01 -.17 -.11 -.22 -.06 -.08 -.04 220 S1 Kluemper et al. (2011) -.05 --- -.08 ------.29 --- 100 S2 Liao et al. (2004) .27 -.19 -.20 -.17 -.38 -.38 -.38 286 Mawritz et al. (2014) .05 ------.04 ------.39 221 Penney et al. (2011) .05 -.15 ------.09 ------239 Sackett et al. (2006) .15 -.08 -.03 -.09 -.41 -.22 -.42 805 Semmer et al. (2010) .25 -.25 -.14 -.36 -.34 -.25 -.42 205 S2 Spector & Zhou (2014) .12 -.03 -.03 -.02 -.33 -.28 -.37 915 Sulea et al. (2012) .19 -.02 ------.22 ------258 Yang & Diefendorff .23 -.11 -.04 -.17 -.22 -.20 -.15 231 (2009) Note. C = Conscientiousness, WD = Workplace deviance, ID = interpersonal workplace deviance, OD = organizational workplace deviance.
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Table 4 Studies, Effect Sizes, and Codings included in the MASEM for Agreeableness r Study Age - Age - Age - Age - A - A - A - N A WD ID OD WD ID OD Alias et al. (2013) .06 -.02 -.03 .00 -.38 -.35 -.40 429 Bowling et al. (2011) .03 -.03 -.02 -.24 -.35 -.36 -.34 193 S1 Bowling et al. (2011) .10 -.23 -.21 -.24 -.31 -.32 -.34 220 S2 Ferris et al. (2009) .05 ------.17 ------.27 230 Iliescu et al. (2015) S2 .08 -.02 ------.09 ------245 Jensen & Patel (2011) .02 .03 .02 .04 -.27 -.23 -.31 517 Kluemper et al. (2011) .02 -.17 -.11 -.22 .05 .05 .05 220 S1 Kluemper et al. (2011) -.13 --- -.08 ------.27 --- 100 S2 Liao et al. (2004) .05 -.19 -.20 -.17 -.35 -.40 -.30 286 Sackett et al. (2006) .14 -.08 -.03 -.09 -.30 -.33 -.21 805 Semmer et al. (2010) .06 -.25 -.14 -.36 -.32 -.42 -.22 205 S2 Spector & Zhou (2014) .10 -.03 -.03 -.02 -.36 -.40 -.32 915 Yang & Diefendorff .20 -.11 -.04 -.17 -.22 -.24 -.19 231 (2009) Note. A = Agreeableness, WD = Workplace deviance, ID = interpersonal workplace deviance, OD = organizational workplace deviance.
Table 5 Studies, Effect Sizes, and Codings included in the MASEM for Neuroticism r Study Age - Age - Age - Age - N - N - N - N N WD ID OD WD ID OD Ferris et al. (2009) -.16 ------.17 ------.08 230 Iliescu et al. (2015) S2 -.05 -.02 ------.20 ------245 Jensen & Patel (2011) -.03 -03 .02 .04 .22 .21 .22 517 Kluemper et al. (2011) -.07 -.17 -.11 -.22 .02 .13 -.10 220 S1 Kluemper et al. (2011) .08 --- -.08 ------.19 --- 100 S2 Liao et al. (2004) -.15 -.19 -.20 -.17 .19 .17 .20 286 Penney et al. (2011) -.20 -15 ------.22 ------239 Sackett et al. (2006) -.12 -.08 -.03 -.09 .32 .29 .26 805 Spector & Zhou (2014) .00 -.03 -.03 -.02 .19 .15 .22 915 Note. N = Neuroticism, WD = Workplace deviance, ID = interpersonal workplace deviance, OD = organizational workplace deviance.
165 AGE AND WORKPLACE DEVIANCE
Table 6 Studies, Effect Sizes, and Codings included in the MASEM for Extraversion. r Study Age - Age - Age - Age - E - E - E - N E WD ID OD WD ID OD Iliescu et al. (2015) S2 -.07 -.02 ------.28 ------245 Jensen & Patel (2011) .06 .03 .02 .04 -.09 -.08 -.09 517 Kluemper et al. (2011) -.12 -.17 -.11 -.22 .06 .02 .09 220 S1 Kluemper et al. (2011) -.14 --- -.08 ------.30 --- 100 S2 Liao et al. (2004) -.13 -.19 -.20 -.17 .01 .06 -.05 286 Sackett et al. (2006) -.12 -.08 -.03 -.09 -.11 -.02 -.15 805 Note. E = Extraversion, WD = Workplace deviance, ID = interpersonal workplace deviance, OD = organizational workplace deviance.
Table 7 Studies, Effect Sizes, and Codings included in the MASEM for Openness to Experience r Study Age - Age - Age - Age - O - O - O - N O WD ID OD WD ID OD Iliescu et al. (2015) S2 .00 -.02 ------.10 ------245 Jensen & Patel (2011) .05 .03 .02 .04 -.08 -.07 -.09 517 Kluemper et al. (2011) .12 -.17 -.11 -.22 .17 .22 .11 220 S1 Kluemper et al. (2011) -.15 --- -.08 ------.02 --- 100 S2 Liao et al. (2004) -.18 -.19 -.20 -.17 -.05 .02 -.12 286 Sackett et al. (2006) -.10 -.08 -.03 -.09 -.06 -.06 -.04 805 Note. O = Openness to Experience, WD = Workplace deviance, ID = interpersonal workplace deviance, OD = organizational workplace deviance.
Results
Relationship Between Age and Workplace Deviance
A small negative but significant correlation between age and workplace deviance was found, r = -0.088, 95% confidence interval (CI) [-0.107, -0.069], p < .001, k =135. There was high variability in the effect size distribution (I2 = 81.137, Q = 711.751, df = 134, p < .001), which justifies the use of a random-effects model. The above effect size is based on self- ratings if a study included both self- and other-ratings of workplace deviance. However, the overall weighted effect size does not change substantially when including other-ratings instead: r = -0.086, 95% CI [-0.105, -0.066], p < .001, k = 135. Hence, we perform all
166 CHAPTER 5 following analyses using self-ratings when both self- and other-ratings were included in a study, in order to increase consistency between studies.
Publication Bias and Sensitivity Analysis
Study effect sizes and precision did not differ significantly according to Begg and
Mazumdar’s (1994) rank correlation (p = .119) and Egger’s regression intercept (p = .077;
Egger et al., 1997). Overall, we can conclude that it is very unlikely that our results were influenced by publication bias. The cumulative analysis showed that the mean weighted r did not change significantly as effect sizes were added one at a time from 1990 to 2016. The overall mean weighted effect size was also not strongly influenced by one individual effect size as indicated by the one-study removed analysis, because the overall effect size only differed slightly (r between -0.086 and -0.091).
Categorical Moderator Analyses
We assessed whether the magnitude of effect sizes was moderated by the form of workplace deviance, but no significant difference emerged, Q(2) = 4.510, p = .105.
Organizational deviance had the largest correlation with age, followed by interpersonal deviance and overall workplace deviance (see Table 8 for results of all categorical moderator analyses). Comparing only interpersonal with organizational workplace deviance did not result in a statistically significant difference either, Q(1) = 1.692, p = .193.
We also examined if the overall weighted effect size was moderated by the questionnaire used to assess workplace deviance. To be able to draw valid comparisons, we only compared the three most commonly used questionnaires with each other (Bennett &
Robinson, 2000; Aquino et al., 1999; Spector et al., 2006). The correlation between age and workplace deviance was significantly moderated by the workplace deviance questionnaire used in the respective studies, Q(2) = 7.662, p = .022. Studies using Bennett and Robinson’s
(2000) questionnaire showed the largest effect size (r = -.123), while studies using Aquino et al.’s (1999, r = -.040) and Spector et al.’s (2006, r = -.061) found smaller average effect sizes.
These results were similar when examining interpersonal or organizational workplace
167 AGE AND WORKPLACE DEVIANCE deviance separately. For organizational workplace deviance, studies using Bennett and
Robinson’s (2000) questionnaire showed the largest effect size (r = -.132, k = 45), followed by Spector and colleagues’ questionnaire (2006, r = -.108, k = 4), and subsequently by
Aquino et al.’s (1999, r = -.031, k = 12). This difference in effect sizes was significant, Q(2) =
8.975, p = .011. For interpersonal workplace deviance, a similar pattern in overall weighted effect sizes emerged: Bennett and Robinson (2000): r = -.116, k = 33; Aquino et al. (1999): r
= -.115, k = 4; Spector et al. (2006): r = -.060, k = 4, but this difference was non-significant,
Q(2) = 1.036, p = .596.
Table 8 Results of the Categorical Moderator Analyses Mean CC CC p Subgroups k N LL UL two- weighted r tailed WD form WD 49 12,009 -.070 -.104 -.035 .000 ID 66 22,027 -.090 -.118 -.062 .000 OD 77 25.371 -.116 -.142 -.089 .000 WD Aquino et al. (1999) 15 3,496 -.043 -.094 .009 .104 questionnaire Bennett & 65 28,673 -.119 -.145 -.092 .000 Robinson (2000) Spector et al. 12 6,238 -.063 -.136 .012 .098 (2006) Rater Self 96 46,200 -.103 -.127 -.080 .000 Other 39 10,428 -.030 -.062 .001 .056 Company Records 3 4,126 -.078 -.108 -.048 .000 Note. Abbreviations: ID = Interpersonal workplace deviance, OD = organizational workplace deviance, WD = overall workplace deviance; k = cumulative number of studies; N = cumulative sample size; mean weighted r = sample size weighted correlation; CCLL and CCUL = lower and upper limit of the 95% confidence interval for r
The source of workplace deviance ratings significantly moderated the relationship between age and workplace deviance, Q(2) = 13.406, p < .01. Self-ratings (r = -.113) showed a larger negative correlation with age than other-ratings (r = -.032) or official company records (r = -.078). The difference in effect sizes is specifically apparent when comparing only self- and other-ratings, Q(1) = 13.400, p < .001. This difference in effect sizes between self- and other-ratings was significant for organizational workplace deviance (self: r = -.138, k
= 56; other: r = -.049, k = 22; Q(1) = 10.476, p < .001) but not for interpersonal workplace deviance (self: r = -.094, k = 54; other: r = -.055, k = 13; Q(1) = 1.763, p = .184).
168 CHAPTER 5
Lastly, we tried to replicate results from Ng and Feldman (2008) who showed that the relationship between age and workplace deviance follows a negative, concave slope. In their meta-analysis based on a small number of studies, they created three age groups (less than 25 years; 25-39 years old; older than 40) based on the average age of the sample, and found that the effect size became more negative with increasing average age of the sample (r = -.01; r = -
.12; r = -.17, respectively). In the current meta-analysis, we could not replicate these results.
We found that the effect size is less negative for studies in the youngest average age category
(for the same average age groups: r = -.06, r = -.10, r = -.09, respectively). This difference in effect sizes was non-significant, Q(2) = 4,926, p = .085.
Continuous Meta-Regressions
A univariate meta-regression showed that the overall weighted effect size distribution was not dependent on the average percentage of females employees in each study (k = 132, ß
= -0.097, p = .128; see Table 9). The pension coverage in the country in which data was collected significantly moderated the relationship between age and workplace deviance (k =
118, ß = -0.002, p = .001), such that the relationship was more negative in countries with higher pension coverage. Lastly, the effect size distribution was significantly dependent on social connections of older individuals (k = 118, ß = -0.004, p = .045).
Table 9 Results of the Continuous Meta-Regressions 2 Predictor k Slope R Slope ptwo-tailed % Female 132 -0.0967 0.00 .128 Pension Coverage 118 -0.0015 0.10 .001 Social Connections 118 -0.0041 0.01 .045 Note. k = cumulative number of studies.
169 AGE AND WORKPLACE DEVIANCE
MASEM Test of Mediations
Table 10 shows the results of the separate meta-analyses conducted in the first stage of the two-stage MASEM. The overall weighted effect sizes found in this smaller subset of studies (k = 4 - 20) resemble those found in the overall age-workplace deviance meta-analysis
(k = 135) and in other meta-analyses of personality and workplace deviance (Berry et al.,
2012, 2007; Salgado, 2002). Negative affect correlates moderately negatively with workplace deviance (r = .274 to .290), and only the subset of studies measuring negative affect finds slightly larger effect sizes for the age-workplace deviance relationships (r = -.142 to -.162).
Table 11 shows the results of the meta-analytic mediations used to test Hypotheses 2 and 3. The chi-square statistic for these mediation models is always 0 and the goodness-of-fit indices for structural equation models do not apply, because all mediation models were just identified (Cheung, 2015). As hypothesized, negative affect, Conscientiousness,
Agreeableness, and Neuroticism all partially or fully mediate the relationship between age and workplace deviance. Only Extraversion and Openness to Experience do not mediate the relationship between age and overall, interpersonal, or organizational workplace deviance (see
Table 11). When testing the three significant personality mediators (i.e., Conscientiousness, agreeableness, Neuroticism) in one model (k = 5, N = 2073), all three indirect effects remain significant. The total effect of age on workplace deviance then is -.078 (95% CI: -.142, -.014), while the direct effect when adding the three mediators to the model becomes non-significant,
-.017 (95% CI: -.086, .053). All three indirect effects are statistically significant;
Conscientiousness: -.034 (95% CI: -.064, -.010), Agreeableness: -.015 (95% CI: -.031, -.004), and Neuroticism: -.017 (95% CI: -.029, -.008). This indicates that the relationship between age and workplace deviance is fully mediated by Conscientiousness, Agreeableness, and
Neuroticism, but that the effect for Conscientiousness is strongest.
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Table 10 Meta-Analytic Results used to Test the Mediation Hypotheses. 2 k N r CILL CIUL p I Age – Negative Affect - WD Age – NA 20 7144 -.114 -.150 -.077 <.001 .559 Age – WD 20 7144 -.142 -.197 -.088 <.001 .816 NA – WD 20 7144 .290 .218 .362 <.001 .917 Age – Negative Affect – ID Age – NA 17 6245 -.119 -.162 -.076 <.001 .640 Age – ID 17 6245 -.155 -.191 -.118 <.001 .495 NA – ID 17 6245 .284 .208 .360 <.001 .912 Age – Negative Affect – OD Age – NA 14 5725 -.109 -.152 -.066 <.001 .604 Age – OD 14 5725 -.162 -.221 -.102 <.001 .807 NA – OD 14 5725 .274 .190 .359 <.001 .920 Age – Conscientiousness – WD Age – C 14 4974 .108 .062 .155 <.001 .622 Age – WD 14 4974 -.082 -.122 -.041 <.001 .486 C – WD 14 4974 -.275 -.326 -.041 <.001 .738 Age – Conscientiousness – ID Age – C 12 4332 .107 .056 .158 <.001 .634 Age – ID 12 4332 -.058 -.095 -.020 <.01 .243 C – ID 12 4332 -.217 -.305 -.020 <.001 .895 Age – Conscientiousness – OD Age – C 15 5119 .121 .079 .163 <.001 .559 Age – OD 15 5119 -.127 -.180 -.073 <.001 .730 C – OD 15 5119 -.315 -.374 -.257 <.001 .816 Age – Agreeableness – WD Age – A 11 4266 .086 .054 .119 <.001 .046 Age – WD 11 4266 -.092 -.145 -.039 <.001 .652 A – WD 11 4266 -.272 -.342 -.203 <.001 .833 Age – Agreeableness – ID Age – A 11 4021 .074 .034 .115 <.001 .279 Age – ID 11 4021 -.062 -.107 -.018 <.01 .421 A – ID 11 4021 -.259 -.371 -.147 <.001 .935 Age – Agreeableness – OD Age – A 11 4251 .087 .055 .119 <.001 .029 Age – OD 11 4251 -.128 -.195 -.061 <.001 .793 A – OD 11 4251 -.266 -.328 -.203 <.001 .789 Age – Neuroticism – WD Age – N 7 3227 -.082 -.129 -.034 <.001 .410 Age – WD 7 3227 -.073 -.124 -.021 <.01 .486 N – WD 7 3227 .198 .137 .259 <.001 .663 Age – Neuroticism – ID Age – N 6 2843 -.060 -.110 -.010 <.05 .386 Age – ID 6 2843 -.052 -.107 .002 >.05 .422 N – ID 6 2843 .146 .048 .245 <.01 .842 Age – Neuroticism – OD Age – N 6 2873 -.082 -.132 -.032 <.01 .402 Age – OD 6 2873 -.094 -.164 -.024 <.01 .697 N – OD 6 2873 .152 .057 .247 <.01 .850 Age – Extraversion – WD Age – E 5 2073 -.068 -.137 .001 >.05 .580
171 AGE AND WORKPLACE DEVIANCE
Age – WD 5 2073 -.076 -.146 -.007 <.05 .584 E – WD 5 2073 -.091 -.180 -.003 <.05 .748 Age – Extraversion – ID Age – E 5 1928 -.078 -.154 -.002 <.05 .598 Age – ID 5 1928 -.066 -.139 .007 >.05 .556 E – ID 5 1928 .030 -.067 .127 >.05 .736 Age – Extraversion – OD Age – E 4 1828 -.069 -.151 .014 >.05 .663 Age – OD 4 1828 -.099 -.194 -.005 <.05 .742 E – OD 4 1828 -.071 -.151 .009 >.05 .606 Age – Openness – WD Age – O 5 2073 -.023 -.121 .074 >.05 .787 Age – WD 5 2073 -.083 -.158 -.008 <.05 .644 O – WD 5 2073 .004 -.084 .093 >.05 .740 Age – Openness – ID Age – O 5 1928 -.044 -.148 .059 >.05 .787 Age – ID 5 1928 -.075 -.150 .001 >.05 .581 O – ID 5 1928 .015 -.081 .112 >.05 .752 Age – Openness – OD Age – O 4 1828 -.025 -.139 .089 >.05 .824 Age – OD 4 1828 -.102 -.199 -.005 <.05 .759 O – OD 4 1828 .014 -.077 .106 >.05 .714 Note. k = cumulative number of studies; N = cumulative sample size; mean weighted r = sample size weighted correlation; CCLL and CCUL = lower and upper limit of the 95% confidence interval for r; NA = Negative affect; C = Conscientiousness, A = Agreeableness, N = Neuroticism, E = Extraversion, O = Openness to Experience; WD = workplace deviance, ID = interpersonal workplace deviance, OD = organizational workplace deviance.
172 CHAPTER 5
No No No No No No Full Full Full Partial Partial Partial Partial Partial Partial Partial Partial Partial Mediation
.005) .055)* .084)* .073)* .011)* .035)* .017)* .038)* .005)* .012)* .013)* .011)* .007)* .004)* ------
.074, .088, .002) .098, .011) .137, .009) .150, .002) - - - - - .166, .161, .194, .094, .144, .122, .173, .109, .153, .153, .199, .158, .199, ------.035 ( .043 ( .044 ( .064 ( .074 ( - - - - - .111 ( .122 ( .134 ( .053 ( .090 ( .069 ( .105 ( .057 ( .082 ( .083 ( .105 ( .083 ( .102 ( ------Direct Effect
< .05. <
.020)* .019)* .016)* .016)* .011)* .023)* .011)* .008)* .013)* .006)* .001)* .001)* p ------
.000, .019) .000, .018) .001, .013, .007) .006, .006) .009, .007) .008, .006) ------( .046, .048, .045, .044, .040, .053, .035, .035, .033, .028, .020, .025, ------.007 ( .005 ( .002 ( .000 .001 ( .000 ( - - - - .032 ( .032 ( .028 ( .029 ( .023 ( .037 ( .023 ( .019 ( .022 ( .016 ( .009 ( .012 ( ------IndirectEffect
.073) Analytic ModelsMediation - - .088)* .118)* .102)* .041)* .020)* .039)* .018)* .021)* .024)* .007)* .005)* .008)* .005)* ------
.107, .002) .139, .007) .150, .001) .180, - - - - .197, .191, .221, .122, .095, .145, .107, .124, .164, .146, .194, .158, .199, ------.128 .061)(.195, - .052 ( .066 ( .075 ( .127 ( - - - Tested Meta Tested - .142 ( .155 ( .162 ( .082 ( .058 ( .092 ( .062 ( .073 ( .094 ( .076 ( .099 ( .083 ( .102 ( Total Effect ------= cumulative sample size; = WD ID interpersonal OD size; = sample = workplace workplace = deviance, deviance, cumulative ix ix
N
N 7144 6245 5725 4974 4332 5119 4266 4021 4251 3227 2843 2873 2073 1928 1828 2073 1928 1828
7 6 6 5 5 4 5 5 4
20 17 14 14 12 15 11 11 11
k
ct, and Direct ct, Effects Direct S for all and
WD ID WD ID OD WD ID OD WD ID OD WD ID OD WD ID OD = cumulative number = studies; cumulative of ndire OD
k – – - – – – – – – – – – – – – – – – . able able 11 Negative Affect Age Age Age Conscientiousness Age Age Age Agreeableness Age Age Age Neuroticism Age Age Age Extraversion Age Age Age toOpenness Experience Age Age Age T Total,I Note are organizational 95%intervals,in workplace Values deviance; brackets * confidence
173 AGE AND WORKPLACE DEVIANCE
Discussion
In the current meta-analysis, which is based on more than 100 studies, we find a small but statistically significant negative correlation between age and workplace deviance. Our findings add to the literature in a number of ways. By including a much large number of studies than previous meta-analyses (Berry et al., 2012, 2007; Ng & Feldman, 2008) we can draw firmer conclusions about the relationship between age and workplace deviance. In addition, we tested two explanatory mechanisms based on the socio-emotional selectivity theory (Carstensen, 1992) and the neo-socioanalytical model of personality change (Roberts
& Wood, 2006). Furthermore, we were able to conduct a finer-grained investigation of important moderating factors, such as a country’s pension coverage or self- versus other- reports of workplace deviance. Below, we will discuss our findings and their implications in more detail.
Socioemotional selectivity theory (Carstensen, 1992) states that as individuals age, they self-select into emotionally meaningful and positive experiences, therefore experiencing less negative affect. For example, older, compared to younger, individuals focus more on goals related to generativity and emotions (Penningroth & Scott, 2012), and show less confrontational behavior when having disagreements with others in the workplace (Davis,
Kraus, & Capobianco, 2009). More importantly, older individuals have been found to appraise and respond to emotional events differently than younger individuals, and to regulate their emotional reaction to those events better (Scheibe & Zacher, 2013). These age differences in emotional experiences at work, their behavioral consequences, and especially the reduced levels of negative affect are one of the explanatory mechanisms for our finding that levels of workplace deviance decrease with age. Individuals experience less negative affect with increasing age, which is, in turn, associated with reduced levels of workplace deviance. An additional and/or alternative explanation is based on the neo-socioanalytical model of personality change, which posits that personality changes across the adult lifespan
(Roberts & Wood, 2006). Personality characteristics that change over time (Agreeableness,
174 CHAPTER 5
Conscientiousness, and Neuroticism; Roberts & Mroczek, 2008; Roberts, Walton, &
Viechtbauer, 2006) have all been found to be negatively related to levels of workplace deviance (Berry et al., 2007). The results of the current meta-analysis confirm the hypothesis that the Big Five personality domain scales Conscientiousness, Agreeableness, and
Neuroticism mediate the relationship between age and workplace deviance. In addition to the mediating effects of negative affect and personality, cohort effects might partly drive the effect of age on workplace deviance. Individuals who share some temporal experience, such as a similar year of birth, could behave differently at work due to the different experiences they have had compared to those born in a later time period (i.e., cohort effect).
Practical, Social and Societal Implications
Various studies have shown that older individuals are disadvantaged in selection and employment decisions (e.g., Ahmed et al., 2012; Bendick et al., 1997; Duncan & Loretto,
2004), which is unjustified from a strictly performance-based view (leaving ethical and moral views aside; Ng & Feldman, 2008). In combination with the current findings, this suggests that organizations that hire and promote a higher percentage of older individuals might reap competitive benefits by observing lower levels of workplace deviance among their employees than those that do not. Considering the costly nature of workplace deviance, organizations should take further steps to reduce age discrimination in employment decisions. For example,
Finkelstein, Burke, and Raju (1995) suggested that highlighting job-relevant information and deemphasizing less important characteristics, such as age, have been shown to reduce age biases in hiring decisions
The percentage of pension coverage in a given country moderated the relationship between age and workplace deviance. Examining the slope of the meta-regression, it becomes apparent that there was no significant relationship between age and workplace deviance in countries that did not have a good pension coverage. In countries where a high percentage of individuals received a pension after retirement, the relationship between age and workplace deviance became significantly more negative. Previous research (e.g., Reisel, Probst, Chia,
175 AGE AND WORKPLACE DEVIANCE
Maloles, & König, 2010; Tian, Zhang, & Zou, 2014) has repeatedly shown that job security works as a protective factor against workplace deviance. Our results now suggest that, especially for older employees who worry more about financial security after retirement than younger employees do, a good pension coverage is associated with lower levels of workplace deviance. Thus, countries and organizations will be more likely to reap the benefits of an age- related decrease in workplace deviance if they implement a good pension system with coverage for all citizens or employees.
In addition to the buffering effect of financial security after retirement, we found a significant moderating effect for the number of social connections individuals have in middle to old age in a given country. In countries in which individuals aged 50 and older had a relatively lower number of significant social connections, age was not significantly correlated with workplace deviance, whereas in countries in which those individuals had a larger number of social connections, older employees were less likely to behave deviantly at work. This suggests that social connections in middle to old age might be another explanatory factor for the age-workplace deviance relationship, and it extends findings suggesting that deviant workplace behavior (i.e., ethical rule breaking) can be predicted from a social bonding perspective according to which individuals with low attachment and involvement with their supervisors are more likely to break rules (Sims, 2002). Our findings show that in older employees, not just strong social ties at work, but possibly also a strong social environment comprising friends and family outside of work can protect against the occurrence of workplace deviance. Overall, these findings highlight the importance of social and financial security in preventing deviant workplace behaviors.
176 CHAPTER 5
Methodological Implications
We found evidence for a few significant moderators of the age-workplace deviance relationship which have important methodological implications for future studies of workplace deviance. The finding that older employees show lower levels of workplace deviance than younger employees was not qualified by a difference between interpersonal and organizational workplace deviance. At least when it comes to analyzing the relationship between age and workplace deviance, distinguishing between interpersonal and organizational workplace deviance seems redundant, which might question the viability of a two factor structure of workplace deviance. This is in accordance with at least one influential study that failed to replicate the proposed two-factor structure of interpersonal and organizational workplace deviance (Lee & Allen, 2002) and suggests that workplace deviance can be treated as one overall construct when its relationship with age is of interest.
We also examined whether the most common questionnaires used to assess workplace deviance moderated the relationship between age and workplace deviance. Compared to the questionnaires by Aquino and colleagues (1999) and Spector and colleagues (2006), the questionnaire developed by Bennett and Robinson (2000) showed the largest negative effect size. This indicates that the three most commonly used workplace deviance questionnaires are differently susceptible to age differences (at least when assessing organizational workplace deviance). One possibility is that the items in Bennett and Robinson's (2000) questionnaire are more sensitive to age-related changes in respondents, such as in personality or emotional perception. However, because these three questionnaires do not differ in their conceptual or overt approach to measuring workplace, further research is needed to explain their differing susceptibility to age differences in respondents.
A long debate has focused on the source of the rating of workplace deviance. It has been argued that self-reports of workplace deviance suffer from a self-enhancing bias, whereas other-reports might not validly assess the extent of workplace deviance due to the hidden nature of these behaviors (Berry et al., 2007; Jones, 2009). A previous meta-analysis
177 AGE AND WORKPLACE DEVIANCE showed that self- and other-ratings are moderately correlated (Berry et al., 2012). In the current meta-analysis, self-ratings of workplace deviance showed a significantly stronger negative correlation with age than other-ratings did (this finding held for organizational workplace deviance, but not for interpersonal workplace deviance when examining these two forms of deviance separately). Given that other-ratings explain relatively little incremental variance over and above self-ratings of workplace deviance and that self-raters admit engaging in more deviant behaviors than what is captured by other-ratings (Berry et al.,
2012), the current results suggest that self-ratings of workplace deviance might capture age- related changes that are associated with lower levels of workplace deviance more accurately.
It might also be that younger employees are more willing to honestly self-report on their deviant behaviors than older employees. Future research should examine this issue in more detail and researchers studying workplace deviance in age-diverse samples should be attentive to these age-related differences and corroborate their findings with different questionnaires and both self- and other-reports.
We also examined whether the percentage of female employees in each study played a moderating role, but found no such effect. While Ng and colleagues (2016) found a significant gender difference in workplace deviance (a finding that was also moderated by age for self-ratings of interpersonal workplace deviance), our findings suggest that the relationship between age and workplace deviance was not qualified by an interaction with gender. This might indicate that age-related changes in levels of workplace deviance are not affected by gender.
Limitations and Future Research
The current meta-analysis has some limitations. First, the studies included in this meta-analysis use a cross-sectional design that does not allow for an inference of causality.
However, age is a constant demographic characteristic, rendering the use of it as a predictor of negative affect, personality, and subsequently workplace deviance reasonable. A similar limitation pertains to the tested mediations, which are all based on correlational data.
178 CHAPTER 5
However, given the vast amount of literature on both personality (Berry et al., 2012; Bolton et al., 2010; Hastings & O’Neill, 2009; O’Neill et al., 2011; Salgado, 2002) and negative affect
(e.g., Aquino et al., 1999; Chen, Chen, & Liu, 2013; Lee & Allen, 2002b; Samnani, Salamon,
& Singh, 2013) as predictors of workplace deviance, it is reasonable to assume that they determine workplace deviance. Second, the moderators pension coverage and social connections were based on country-level characteristics. It might be that those characteristics are not generalizable to the individual employees included in the studies. In addition to that, these country-level characteristics were measured in 2015, while the studies included in our meta-analysis were conducted between 1990 and 2016. Unfortunately, these country-level data were not available for all years of publication. Thus, these results should be interpreted with caution until future studies have examined whether those country-level characteristics also moderate the relationship between age and workplace deviance on an individual level.
Lastly, the analyses used to test the mediating effect of the personality domain scales
Extraversion and Openness to Experience as well as the full mediation model with
Conscientiousness, Agreeableness, and Neuroticism, are based on a small number of included studies (k = 4 -5) and should therefore be interpreted with caution. If possible, future research should investigate these mediating mechanisms with a larger number of included studies.
Another limitation pertaining to this issue is that we would have liked to test one complete mediation model with all three significant personality mediators and negative affect to pit predictions based on the socio-emotional selectivity theory and the neo-socioanalytical model of personality change against each other. However, none of the studies included in this meta- analysis measured both personality and negative affect in the same sample. Future research should therefore investigate the extent to which age-related changes in negative affect or in personality (or in both) explain the relationship between age and workplace deviance.
Conclusion
Age is negatively related to workplace deviance, and personality (i.e.,
Conscientiousness, Agreeableness, Neuroticism) and negative affect mediate this relationship.
179 AGE AND WORKPLACE DEVIANCE
As such, these results demonstrate the underlying mechanisms for the negative relation between age and workplace deviance for the first time. As older workers are disadvantaged in employment and promotion decisions (e.g., Ahmed, Andersson, & Hammarstedt, 2012), despite having similar job performance levels as younger employees (Ng & Feldman, 2008), we hope that our findings could make organizations further aware of the unfair selection bias against older workers. In light of our findings, hiring and selecting older employees might even provide competitive benefits to organizations. This meta-analysis further provides valuable insights into the study of workplace deviance in age-diverse samples, because it highlights several important methodological and practical moderators of the relationship between age and workplace deviance.
180
181 CHAPTER 6
CHAPTER 6 DOES GENDER MATTER? FEMALE REPRESENTATION ON CORPORATE BOARDS AND FIRM FINANCIAL PERFORMANCE: A META- ANALYSIS
This chapter is based on Pletzer, J. L., Nikolova, R., Kedzior, K. K., & Voelpel, S. C. (2015). Does gender matter? Female representation on corporate boards and firm financial performance – A meta-analysis. PloS One, 10, e0130005. doi: 10.1371/journal.pone.0130005 A paper draft was presented at the Academy of Management Conference 2015.
182 CHAPTER 6
Abstract
In recent years, there has been an ongoing, worldwide debate about the representation of females in companies. Our study aimed to meta-analytically investigate the controversial relationship between female representation on corporate boards and firm financial performance. Following a systematic literature search, data from 20 studies on 3097 companies published in peer-reviewed academic journals were included in the meta-analysis.
On average, the boards consisted of eight members and female participation was low (mean
14%) in all studies. Half of the 20 studies were based on data from developing countries and
62% from higher income countries. According to the random-effects model, the overall mean weighted correlation between percentage of females on corporate boards and firm performance was small and non-significant (r = .01, 95% confidence interval: -.04, .07).
Similar small effect sizes were observed when comparing studies based on developing vs. developed countries and higher vs. lower income countries. The mean board size was not related to the effect sizes in studies. These results indicate that the mere representation of females on corporate boards is not related to firm financial performance if other factors are not considered. We conclude our study with a discussion of its implications and limitations.
Keywords: gender diversity, organizational performance, board of directors, meta- analysis
183 GENDER AND PERFORMANCE
Introduction
Advancing gender equality and female representation in corporate governance has increasingly become the focus of societal and political debates in various countries (Pande &
Ford, 2011). Despite extensive efforts to increase women’s presence on corporate boards, men still dominate the corporate world. The financial effects of increased female representation on corporate boards may crucially determine if, and how, regulations to promote females to higher positions are implemented, because pursuing financial success is an innate characteristic of every company. While a number of scientific studies have investigated the relationship between gender diversity and firm financial performance, their conclusions are equivocal (Kochan et al., 2003; Webber & Donahue, 2001). These empirical discrepancies have led to a lack of conclusive evidence about the relationship between increased female representation and firm performance, creating uncertainty for policy makers,
CEOs, and investors around the world. Owing to the conflicting evidence from primary studies, systematically summarizing the existing data on the topic in a quantitative meta- analysis has merit. While our general research question is similar to that of Post and Byron
(Post & Byron, 2015), the methodological and analytical approach differs substantially between the two analyses. Our study aims to investigate the relationship of interest with a different, more rigorous and controlled methodological approach, and subsequently compares the results of the two meta-analyses. Investigating this relationship in a different sample and with different operationalizations of the variables (compared to Post & Byron, 2015) is especially important, because, in their analysis, the overall mean weighted correlation between female participation on boards and firm performance was very small (only marginally different from zero). Thus, this paper investigates the general relationship between female representation and firm performance using a new and different methodological approach, highlights our additional contribution to the literature, and compares the similarities and differences between the two analyses.
Literature Overview
184 CHAPTER 6
A board of directors monitors the activities of an organization or company. It sets the corporate strategy, appoints and supervises senior management, and functions as the main corporate governance mechanism. The role of the board in determining the corporate strategy therefore influences firm performance. Since diversity is often considered a double-edged sword (e.g., Milliken & Martins, 1996), meaning that increased diversity can result in advantages and disadvantages regarding desired outcomes, a board composed of diverse directors affects firm performance either positively or negatively. Diversity’s positive and negative effects could also neutralize each other, or could depend on how it is managed (van
Knippenberg, De Dreu, & Homan, 2004). Along these lines, a meta-analysis by Webber and
Donahue (2001) examined the effects of diversity on work group performance in a sample of
45 effect sizes. Low job-related (e.g., age, gender) and highly job-related diversity (e.g., educational background) were measured, but both failed to show a significant relationship with work group performance.
Further, primary studies also do not show a clear consensus on whether gender diversity benefits or disadvantages firm performance (Jackson, Joshi, & Erhardt, 2003; Miller
& del Carmen Triana, 2009). At first glance, the relationship between female representation on corporate boards and firm financial performance shows a similar pattern to that of the general diversity-performance relationship, being either positive (Mahadeo, Soobaroyen, &
Hanuman, 2011), negative (Pathan & Faff, 2013), or non-significant (Strøm, D’Espallier, &
Mersland, 2014). Thus, it remains unclear if increased female representation on corporate boards is associated with firm performance, and, if so, in which direction.
Advocates of greater female representation on corporate boards usually rely on two lines of arguments: the ethical or the business case for diversity (Robinson & Dechant, 1997).
The former argues that women should be considered for leadership positions for equality reasons. The aim is therefore not directly to increase performance, but rather that greater female representation is considered a positive and just result in itself (Brammer, Millington,
& Pavelin, 2007). Thus, a higher proportion of females on boards might not necessarily be
185 GENDER AND PERFORMANCE related to better firm performance, but would reflect that boards with more females closely represent the ‘real world’, while other factors than gender alone contribute to better financial outcomes. The business case for diversity holds that if a board comprises heterogeneous directors, diversity leverages financial growth and success (Robinson & Dechant, 1997), indicating that a higher proportion of females could be related to better firm performance. A final outcome, not explained by either of the cases above, would be a negative relationship between a higher proportion of females on boards and lower firm performance. The aim of this article is to summarize the already existing quantitative evidence and attempt to explain the results in light of these cases.
Positive Effects of Increased Female Representation on Firm Performance
The business case for diversity holds that diverse team members improve corporate governance by introducing broader knowledge bases and experiences (Fondas & Sassalos,
2000; Robinson & Dechant, 1997). Accordingly, the cognitive resource model suggests that as (gender) diversity in groups increases, the available cognitive resources increase as well (S.
E. Jackson, May, & Whitney, 1995; McLeod, Lobel, & Cox, 1996). If used effectively, these diverse perspectives can contribute to a more thorough search for alternative solutions to problems because they introduce new perspectives to the boardroom (Watson, Kumar, &
Michaelsen, 1993). These diverse perspectives also foster a critical analysis of complex problems, prevent premature decision-making (Carter, Souza, Simkins, & Simpson, 2010;
Farrell & Hersch, 2005; Milliken & Martins, 1996; van Knippenberg et al., 2004), and develop creative and innovative solutions (Bassett-Jones, 2005). Hence, increased female representation on corporate boards should improve firm financial performance through the diverse perspectives introduced to the boardroom.
Another essential argument in support of the business case for diversity is that women introduce useful female leadership qualities and skills to the boardroom. These include, for example, risk averseness and less radical decision-making (Croson & Gneezy, 2009;
Jianakoplos & Bernasek, 1998), as well as more sustainable investment strategies (Charness
186 CHAPTER 6
& Gneezy, 2012). In addition, female leaders fulfil their leadership roles in a more transformational way than their male counterparts, distinguishing themselves especially through their encouraging and supportive treatment of colleagues and subordinates (i.e., individualized consideration; (Eagly, Johannesen-Schmidt, & van Engen, 2003). Females are also said to value their responsibilities as directors higher, which is associated with more effective corporate governance (Terjesen, Sealy, & Singh, 2009). Furthermore, diversity on corporate boards generally benefits organizations, by providing wider and better connections and ties to suppliers, organizations, and consumers, which decrease market uncertainties and dependencies (Miller & del Carmen Triana, 2009). In sum, an increased female presence on corporate boards is associated with the introduction of new desirable leadership skills and a variety of strategic advantages for companies. Following this reasoning, we expect a positive relationship between increased female representation and firm financial performance.
Negative Effects of Increased Female Representation on Firm Performance
Individuals are likely to perceive others and themselves in terms of salient social categories, such as gender, thereby creating in- and out-groups (Tajfel, 1978). These categorization tendencies, which might lead to heightened gender salience and a perceived lack of alignment with the group’s stereotypes (Abrams, Thomas, & Hogg, 2011), can compromise functional team processes when demographic subgroups emerge. If the emerging subgroups on corporate boards are based on gender, communication and cooperation might be impaired (van Knippenberg et al., 2004), leading to increased conflicts between board members. The probability of conflict might be further enhanced if the directors identify stronger with the opinions of fellow directors of the same gender (Richard, Barnett, Dwyer, &
Chadwick, 2004), or if the introduction of new perspectives, previously mentioned as one of diverse groups’ advantages, backfires (Jehn, Northcraft, & Neale, 1999). In turn, this potential for interpersonal conflicts might retard the decision-making process and lead to a lack of cohesion between board members and to decreased strategic consensus (Amason, 1996;
Knight et al., 1999), hindering corporate boards’ effectiveness. In fast-paced environments,
187 GENDER AND PERFORMANCE such as on corporate boards where strategic decisions need to be taken quickly, conflict-free communication is crucial to maintain effective performance (Williams & O’Reilly, 1998).
And even if these issues can be overcome, the additional time and resources spent on solving them might decrease group and organizational performance (Kyereboah-Coleman, 2006).
These far-reaching potentials for impaired team processes might especially challenge females, who struggle to participate and maintain their standing in the already male- dominated boardroom (Tuggle, Sirmon, & Bierman, 2011) and are at risk of experiencing role ambiguity and role conflict, because they do not conform to typical gender roles in leadership
(Koenig, Eagly, Mitchell, & Ristikari, 2011). Such females might be perceived as “tokens” to meet society’s expectations or those of important stakeholders, and could therefore be marginalized and not be taken seriously on the board (Kanter, 1977), which might subsequently hinder their and the entire board’s performance (S. E. Jackson & Schuler, 1985;
Tubre & Collins, 2000).
All in all, increased female representation could potentially lead to decreased firm financial performance due to a number of strategic disadvantages, increased interpersonal conflicts, and their associated negative consequences. Following this reasoning, we expect a negative relationship between increased female representation and firm financial performance.
In conclusion, it is difficult to determine the relationship between female representation on boards and firm performance a priori. Summarizing all studies measuring the relationship between female representation on corporate boards and firm financial performance could provide substantive evidence to address the question whether increased female representation on corporate boards alone is positively or negatively related to firm financial performance. The meta-analysis by Post and Byron (2015) provided the first systematic summary of this relationship in studies selected from a range of electronic databases and unpublished sources. Using 140 studies (92 published, 48 unpublished), they show that the relationship is very small (r = 0.047 between female representation and
188 CHAPTER 6 accounting returns, and r = 0.014 between female representation and market performance), and that it might depend on moderators, such as shareholder protection or gender parity in a given country. As opposed to Post and Byron (2015), our study follows a more rigorous and controlled methodological approach by investigating the relationship between percentage of females on corporate boards and firm financial performance, operationalized as return on assets (ROA), return on equity (ROE), and Tobin’s Q(Q), by means of a meta-analysis of articles published in peer-reviewed academic journals. These narrow operationalizations of the variables could increase the certainty with which theoretical and practical implications can be deduced. In light of the study by Post and Byron (2015), we also aim to compare the results of the two meta-analyses. A systematic investigation of the published literature could also reveal potential moderators of such a relationship based on systematic differences between various studies in terms of their source of data and company characteristics, which could only be determined post hoc once we had located and coded all the available data.
Based on the studies reviewed above and the meta-analysis of Post and Byron (2015), we hypothesized that female representation on corporate boards is either positively or negatively related to firm financial performance, but that the magnitude of such a relationship is likely to be small.
189 GENDER AND PERFORMANCE
Method
Systematic Search Strategy
A systematic literature search was conducted in EBSCO on March 7, 2014, using the search strategy described in Table 1. The databases and the search terms we utilized differ from those used by Post and Byron (2015). Therefore, the two meta-analyses are based on a different sample of articles. In addition, we conducted a hand-search of the reference sections of review articles in this field and of Google Scholar, using the terms listed in Table 1 between March 7 and May 12, 2014. Unlike Post and Byron (2015), we only searched for
(and included) studies published in peer-reviewed academic journals.
Table 1 Search Terms and Databases (All Searches conducted in English) Number of Search terms and limits Databases sources (k) (1986-March 2014) 325 [Subject OR Title (“gender diversity” OR gender OR • PsycInfo female OR wom*n OR "board diversity" OR "board of • EconLit director*" OR "board structure")] • Business AND Source [Subject OR Title (“organi*ation* performance” OR Premier “firm performance” OR "financial performance" OR • Academic “company performance”)] Search limits: Academic Articles, English Language Premier
Study Selection
The study selection process is summarized on the PRISMA flowchart (Moher,
Liberati, Tetzlaff, & Altman, 2009) in Figure 1. Of the 325 sources identified during the electronic search (Table 1) and the 17 sources identified through the hand search, 52 studies met the inclusion criteria and were fully examined (Figure 1). The studies selected for the final meta-analysis had to have a quantitative design and report the Pearson product moment correlation coefficient, r, between the percentage female representation on boards of directors and firm performance (measured as ROA, ROE, or Q), with the number of observations
(number of firms × total length of data collection in years) used as the sample size. Studies not reporting the correlation coefficient, but including the variables required in our analysis,
190 CHAPTER 6 were also included in our sample if the authors (contacted via email) provided these correlations. Studies were excluded if other performance measures were used (e.g., return on investment, firm value). We focused on ROA, ROE, and Q, because they are relatively objective and the most commonly used indicators of organizational performance.
Figure 1 Study Assessment and Exclusion Criteria
k = 325 records published in 1986- 2014 from database k = 17 records identified searching (Table 1) through “hand-search” IDENTIFICATION
SCREENING k = 293 records after duplicates removed
k = 293 records k = 241 excluded ELIGIBILITY (titles/abstracts) screened by RN k = 32/52 (62%) articles excluded:
• k = 9: Inappropriate k = 52 full-text articles performance measures assessed by RN/JP • k = 9: Inappropriate gender INCLUDED diversity measure (Blau’s index, dichotomous coding of gender) • k = 4: Gender diversity was not measured • k = 3: Data for owners or entrepreneurs • k = 2: Does not measure the relationship between gender diversity and performance
• k = 2: Not found in the k = 20 studies included database
in the quantitative • k = 2: Does not report meta-analysis sample size • k = 1: Qualitative design
Note. k = number of studies. Return on investments and firm value were not used because there might be large differences in these measures due to economic differences between countries or due to strategic
191 GENDER AND PERFORMANCE orientations. Unlike in Post and Byron (2015), studies were excluded from our meta-analysis if female representation was measured as a dichotomous variable (presence vs. absence of females on board), or if they used measures that did not explicitly target the representation of females (e.g., the Blau Index or the Shannon Index), because they might bias the analysis. We also excluded studies that assessed female representation in an inappropriate body (such as the management or owners). This approach allowed us to achieve a reliable measure of female participation on corporate boards.
Definition of Variables
Firm performance measure. Firm performance was measured with three variables.
First, ROA is computed by dividing the “earnings before extraordinary income and preferred dividend in financial year” by the “average of book values of total assets at the beginning and at the end of [a] financial year” (Haslam, Ryan, Kulich, Trojanowski, & Atkins, 2010). The
ROA measures the company’s production of “accounting based revenues in excess of actual expenses from a given portfolio of assets measured as amortized historical costs” (Carter et al., 2010). Second, ROE is computed by dividing the company’s “earnings before extraordinary income and preferred dividend in [a] financial year” by the “average of book values of common equity at the beginning and at the end of a financial year” (Haslam et al.,
2010). Thus, this measure assesses the returns to the company’s shareholders. ROE and ROA share a characteristic in that they are both “backward looking” accounting-based measures, meaning that they are based on the company’s self-reported financial performance in the recent past (Haslam et al., 2010). Third, Tobin’s Q is a market-based firm performance index calculated by dividing the firm’s “year-end market capitalization and average of book values of total debt at the beginning and at the end of [a] financial year” by the “average of book values of total assets at the beginning and at the end of [a] financial year” (Haslam et al.,
2010). This measure is increasingly used in diversity research and scholars argue that it is more reliable than accounting-based measures (Campbell & Mínguez-Vera, 2008), because it represents a “forward looking” measure, meaning that it reflects the future potential of a
192 CHAPTER 6 firm’s performance (Haslam et al., 2010). Beyond this, it is a standardized measure with intuitive interpretation criteria: If the ratio is greater than one, the firm has a higher ability to create value by effectively allocating its resources, indicating a high competitive advantage for that firm (Rose, 2007). In contrast to ROE and ROA, this measure lacks an accounting convention bias, because it is considered objective by not relying on self-reported data
(Campbell & Mínguez-Vera, 2008).
Female representation. Female representation was measured as the percentage of females on corporate boards.
Moderator variables. In addition to performance measures and female representation on boards, the following variables were included in the current analysis due to the relevant data in all, or most, of the studies:
1. Country development and income. Using the countries in which data were collected,
we classified studies into dichotomous groups based on their economic development
status (developed: k = 9; developing: k = 10) and their Gross National Income per
capita (GNI; high income: k = 13; low income: k = 6; United Nations, 2012).
2. Mean board size. This variable was a scale measure of the mean number of directors
on boards.
Data Extraction
Data were extracted independently by three authors of this study from k = 20 studies
(Post and Byron, 2015, included 13 of the 20 studies from our sample in their analysis), resulting in 34 coded effect sizes, and any inconsistencies were resolved during discussions.
The authors of 16 studies were contacted via e-mail by the third author and seven provided additional data (either the sample size used for correlations or the correlation coefficients if they were not reported). Table 2 lists the study characteristics and effect size data.
Table 2 Study Characteristics and Effect Size Data in 20 Studies included in the Meta-Analysis Study Period CO DEV/DC GNI PM %F r N No. Firms Board Abdillahi & Manini (2013) 2007 - 2011 KN DC LI ROA --- .476 45 9 ---
193 GENDER AND PERFORMANCE
Abdillahi & Manini (2013) 2007 - 2011 KN DC LI ROE --- .094 45 9 --- Ahern & Dittmar (2012) 2001 - 2009 NO DEV HI Q 24.6 -.067 1074 248 5.5 Bøhren & Staubo (2013) 2000 - 2009 NO DEV HI ROA 17.0 .029 1560 ~274 5.6 Dale-Olsen et al. (2013) 2003 - 2007 NO DEV HI ROA 14.6 .090 1279 128 7.6 Darmadi (2011) 2007 IN DC LI ROA 12.0 -.070 169 169 8.4 Darmadi (2011) 2007 IN DC LI Q 12.0 -.160 169 169 8.4 Galbreath (2011) 2005 - 2007 AU DEV HI ROA 9.0 -.060 151 151 7.6 Galbreath (2011) 2005 - 2007 AU DEV HI ROE 9.0 .220 151 151 7.6 Garba & Abubakar (2014) 2004 - 2009 NI DC LI ROA 9.5 .139 72 12 9.1 Garba & Abubakar (2014) 2004 - 2009 NI DC LI ROE 9.5 .164 72 12 9.1 Garba & Abubakar (2014) 2004 - 2009 NI DC LI Q 9.5 .046 72 12 9.1 Haslam et al. (2010) 2001 - 2005 UK DEV HI ROA 8.4 .040 486 97 11.3 Haslam et al. (2010) 2001 - 2005 UK DEV HI ROE 8.4 .030 486 97 11.3 Haslam et al. (2010) 2001 - 2005 UK DEV HI Q 8.4 -.110 486 97 11.3 Julizaerma & Sori (2012) 2008 - 2009 ML DC HI ROA 10.6 -.015 280 280 7.6 Kyereboah-Coleman (2006) 1995 - 2004 GH DC LI ROA 37.4 .011 520 52 6.2 Lückerath-Rovers (2011) 2055 - 2007 NL DEV HI ROE 4.0 .328 297 99 7.8 Mahadeo et al. (2011) 2007 MA DC HI ROA 3.1 .337 42 42 9.6 Pathan & Faff (2013) 1997 - 2011 US DEV HI ROA 7.9 -.140 2640 212 12.7 Pathan & Faff (2013) 1997 - 2011 US DEV HI ROE 7.9 -.100 2640 212 12.7 Pathan & Faff (2013) 1997 - 2011 US DEV HI Q 7.9 -.150 2640 212 12.7 Rodríguez-Domínguez et al. (2010) 2004 - 2006 ES DEV HI ROA --- -.020 288 96 --- Rodríguez-Domínguez et al. (2010) 2004 - 2006 ES DEV HI ROE --- -.025 288 96 --- Rodríguez-Domínguez et al. (2010) 2004 - 2006 ES DEV HI Q --- .061 288 96 --- Shafique et al. (2014) 2008 - 2012 PK DC LI ROA 33.3 -.049 30 6 --- Shukeri et al. (2012) 2011 ML DC LI ROE 9.8 .094 300 300 7.4 Strøm et al. (2014) 1998 - 2008 WW ------ROA --- -.004 497 329 --- Strøm et al. (2014) 1998 - 2008 WW ------ROE --- .003 462 329 --- Van Ness et al. (2010) 2006 - 2007 US DEV HI ROA 14.0 .100 185 185 10.5 Wellalage & Locke (2013) 2006 - 2010 SL DC LI ROA 7.4 -.011 440 88 7.3 Wellalage & Locke (2013) 2006 - 2010 SL DC LI Q 7.4 -.430 440 88 7.3 Zhou et al. (2012) 2000 - 2009 CH DC HI ROA 12.3 -.012 3197 ~320 2.2 Zhou et al. (2012) 2000 - 2009 CH DC HI Q 12.3 .087 3197 ~320 2.2 Note. Abbreviations: AU = Australia; Board = mean size of the board; CH = China; CO = country of data collection; DC = developing country; DEV = developed country; ES = Spain; GH = Ghana; GNI = Gross National Income Classification; HI = high-income; IN = Indonesia; KN = Kenya; LI = low-income; N = number of observations (number of firms × total length of data collection in years); MA = Mauritius; ML = Malaysia; NI = Nigeria; NL = Netherlands; NO = Norway; No. Firms = Number of firms in sample; Period = time frame in which data were collected; PK = Pakistan; PM = Performance measure; SL = Sri Lanka; US = United States; WW = Worldwide; %F = percentage of female board members.
194 CHAPTER 6
Data Analysis (Meta-Analysis)
The effect size used in the current meta-analysis was the Pearson product moment correlation coefficient, r. The interpretation criteria for the absolute magnitude of r are: .1 small, .3 medium, and .5 large (Cohen, 1988). The meta-analysis was computed using
Comprehensive Meta-Analysis 2.0 (CMA; Biostat, USA) according to the random-effects model with inverse-variance weights (Borenstein et al., 2009). The analysis was conducted in the following steps (Borenstein et al., 2009):
1. The effect size data (r and N) were reported for each study. Since the variance of r is
biased based on the magnitude of r, CMA converts r to Fisher’s z, computes all
subsequent analyses on Fisher’s z, and converts the final results back to r.
2. Each effect size (Fisher’s z) was weighted according to the inverse-variance method
(inverse of the sum of within- and between-study variance; (DerSimonian & Laird,
1986).
3. The overall mean weighted effect size of all studies was computed according to the
random-effects model, where overall r is the sum of the product of all r (expressed as
Fisher’s z) and weights divided by the sum of all weights. This model was used,
because we assumed that the effect sizes would differ between studies in the analysis
due to differences in study characteristics and because we only identified a random
sample of all studies on this topic in our literature search.
The heterogeneity between study effect sizes was computed using a Q statistic and an I2 index, where I2 = 100%×(Q-df)/Q with df = k-1 and k = number of studies (Borenstein et al., 2009). The I2 index quantifies the variability in the effect sizes due to real (rather than chance) differences between studies. This variability can be interpreted as low (25%), moderate (50%), or high (75%) heterogeneity due to real differences between studies
(Higgins, Thompson, Deeks, & Altman, 2003).
195 GENDER AND PERFORMANCE
Publication Bias Analyses
Studies with statistically significant and high effect sizes are more likely to be published in academic journals (Borenstein et al., 2009). Such a publication bias could have inflated the result of our meta-analysis, which focused specifically on findings published in academic journals. Thus, we controlled for publication bias using methods available in CMA.
Specifically, publication bias could be present if:
1. Rosenthal’s Fail Safe-N is low, meaning that it takes only a few theoretically missing
studies with low effect sizes to nullify the result of a meta-analysis (Rosenthal, 1979),
2. a funnel plot of standard error by Fisher’s z in each study is not symmetrical (Egger et
al., 1997) and mathematically correcting for symmetry (using the trim-and-fill
analysis) changes the interpretation of the overall analysis (Duval & Tweedie, 2000),
3. the study effect sizes and precision differ systematically according to the statistically
significant Begg and Mazumdar correlation (Begg & Mazumdar, 1994) and Egger’s
regression (Egger et al., 1997).
Sensitivity and Moderator Analyses
The stability of the overall mean weighted r over time was investigated by adding one study at a time to all previous studies (cumulative analysis). The influence of individual studies on the overall mean weighted r was investigated by removing one study at a time from the overall analysis (one-study removed analysis). The moderator analyses (subgroup analyses and univariate meta-regressions) were used to test the influence of systematic differences between studies on the overall mean weighted r.
Results
Study Characteristics
A total of 20 studies with 34 effect sizes were included in the current meta-analysis.
Firm performance was measured according to ROA in 85% of effect sizes (k = 17), ROE in
45% (k = 9), and Q in 40% (k = 8; Table 3). All 34 effect sizes were based on an average of
734 observations from 146 firms collected in slightly more than five years (Table 3). On
196 CHAPTER 6 average, the boards in those firms consisted of almost eight members with a low female representation (14%; Table 3). Half of all effect sizes were based on data from developing countries and data in 62% of the studies came from high income countries (Table 3).
Table 3 Descriptive Statistics for k=20 Studies included in the Current Meta-Analysis k studies (% of 20) # developing countries (% within 9 outcome) (45%) WESP # developed countries (% within 10 outcome) (50%) # high-income countries (% within 13 outcome) (62%) GNI # low-income countries (% within 6 outcome) (32%) Mean number of 734
observations (SD) (875) 146 Mean number of firms (SD) (104) 7.89 Mean board size (SD) (2.49) 13.82 Mean % female (SD) (9.52) Mean data collection period 5.32
(SD) (3.99) Note. Abbreviations: GNI = Gross National Income per capita; Q = Tobin’s Q; ROA = Return on Assets; ROE = Return on Equity; SD = standard deviation; WESP = World Economic Situations and Prospects classification.
Relationship between Female Representation and Firm Performance
There was a small positive, but not statistically significant, relationship between the percentage female representation on corporate boards and the combined mean of the three firm performance measures; overall mean weighted r = .01, 95% confidence interval, CI [-.04,
.07], p = .598, k = 20 (Figure 2). High variability in effect sizes among the 20 studies existed
(I2 = 87%, Q = 142.84, df = 19, p < .001).
197 GENDER AND PERFORMANCE
Figure 2 Forest Plot of the Association between Percentage Female Representation on Corporate Boards and Firm Performance
Note. ‘Correlation’ refers to the weighted Pearson product moment correlation coefficient, r. ‘Combined’ refers to the mean effect size in studies using multiple measures of firm performance. ‘Total’ refers to the total number of observations per study (number of firms × number of years). The diamond depicts the overall mean weighted effect size r of all k=20 studies. There is a small positive, but not statistically significant, relationship between percentage female representation on corporate boards and firm performance (overall mean weighted r = .01, 95% confidence interval, 95%CI: -.04, .07).
Publication Bias Analyses
Since the overall result of our analysis was not statistically significant, Rosenthal’s
Fail-Safe N was not applicable. Nevertheless, there was little evidence of publication bias in the current analysis, because the funnel plot (Figure 3) was mathematically symmetrical around the central vertical line (corresponding to the overall mean weighted effect size) according to the trim-and-fill analysis. Thus, the overall mean weighted effect size in the analysis (unfilled diamond) and the overall effect corrected for potential missing studies
(filled diamond) are aligned and no filled circles (theoretically missing studies) are shown on the plot (Figure 3). The symmetry is particularly evident in the area towards the top of Figure
3, showing the studies (unfilled circles) with the lowest estimated variability in effect sizes, and, thus, the highest precision. These studies contributed the most weights to the calculation of the overall mean weighted effect size in the current meta-analysis. Therefore, confirming
198 CHAPTER 6 the results on the forest plot (Figure 2), the studies with the most weights had either positive, negative, or close to null effect sizes, indicating that there was no preference for high positive, or high negative, effects in the current meta-analysis. Although the studies towards the bottom of Figure 3 appear less symmetrically distributed, these had high estimated variability of effect sizes, low weights, and, thus, little influence on the overall mean weighted effect size in the current analysis. Finally, study effect sizes and precision did not differ systematically according to the non-significant Begg and Mazumdar correlation coefficient (p = .770) and
Egger’s regression intercept (p = .374).
Figure 3 Funnel Plot of the Estimated Variability (Standard Error of the Mean, SEM) and Effect Size r (expressed as Fisher’s z) in each Study
Note. This plot shows that the effect sizes in the individual studies (circles) were symmetrically distributed around the overall mean weighted effect size shown on the vertical line.
Sensitivity Analyses
The cumulative analysis showed that the overall mean weighted r remained consistently small and non-significant as studies were added one at a time (based on publication date) from 2006 to 2014 (Figure 4). Similarly, the overall mean weighted r was not influenced by any one study, because it remained small and non-significant as one study at a time was removed from the overall analysis.
199 GENDER AND PERFORMANCE
Figure 4 Forest Plot of the Cumulative Analysis
Note. ‘Combined’ refers to the mean effect size of studies that have used multiple measures of firm performance. ‘Total’ refers to the total number of observations (number of firms × number of years) as one study is added to all previous studies in each row. The plot shows how the overall mean weighted effect size r (referred to as ‘Point’) changes as each study is added over time to all previous studies. The diamond depicts the overall mean weighted effect size r of all k = 20 studies.
Subgroup Analyses and Meta-Regressions
According to a subgroup analysis, the overall outcome of the current meta-analysis was not dependent on the performance measure. Specifically, the overall mean weighted r remained consistently small and non-significant when studies were grouped according to individual performance indicators (Table 4).
200 CHAPTER 6
Table 4 Results of the Moderator Analyses and the Meta-Regression Subgroups k studies (% of 20) Mean weighted r (95%CI) ptwo-tailed Performance Measure ROA 17 (85%) .00 (-.05, .05) .861 ROE 9 (45%) .08 (-.02, .19) .125 Q 8 (40%) -.10 (-.21, .02) .107 WESP Development Developed countries 9 (47%) -.02 (-.06, .10) .661 Developing countries 10 (53%) .01 (-.07, .10) .753 GNI Lower income countries 6 (32%) -.02 (-.17, .12) .750 Higher income countries 13 (68%) .03 (-.03, .10) .310 Meta-regression predictor k studies Slope Slope ptwo-tailed Mean Board Size 16 -.01 .400
Similarly, the overall mean weighted r remained consistently small and non- significant when studies were grouped according to either country development (developed vs. developing) or country income (lower vs. higher income); Table 4. Finally, as shown in Table 4, the univariate meta-regression showed that the weighted effect sizes in individual studies (outcome) were not dependent on the mean board size (predictor).
Discussion
The main finding of the current study, based on data from 20 studies (34 effect sizes) published only in peer-reviewed academic journals, is that the relationship between the percentage of female directors on corporate boards and firm financial performance is consistently small and non-significant. The general magnitude of this result is in line with findings of Post and Byron (2015), who, based on data from 140 published and unpublished studies, also found a small correlation between gender diversity on corporate boards and firm financial performance. This is especially noteworthy, because both meta-analyses are based on different study samples and different operationalizations of the main measures (female representation and firm performance), providing further evidence to conclude that female representation and firm performance are not strongly associated. Interestingly, the two meta- analyses differ in that Post and Byron (2015) find a statistically significant correlation between increased gender diversity on corporate boards and higher accounting returns. But
201 GENDER AND PERFORMANCE concluding that a higher female representation on corporate boards has practical implications for the generation of profits from assets and investments seems debatable due to the overall small effect size. By testing the relationship of interest with our more rigorous and controlled methodological approach (for example, by including only peer-reviewed and published studies), we provide further evidence that female representation on corporate boards is not associated, positively or negatively, with firm performance. Although both meta-analyses indicate only a very small correlation, primary research should further investigate the relationship between boards’ gender composition and firm performance. This is because, as argued below, the relationship might be too complex to be investigated on a univariate level.
In our international sample, female representation on corporate boards was not significantly related to firm financial performance, as measured by the “backward-looking” measures ROA and ROE and the forward-looking measure Tobin’s Q, if we did not control for any other factors. This result is in line with other primary studies and meta-analyses (Ali,
Ng, & Kulik, 2013; Webber & Donahue, 2001; Wellalage & Locke, 2013), indicating only a small association between (gender) diversity and firm financial performance, while contrasting individual studies that find either a positive (Mahadeo et al., 2011) or negative relationship (Pathan & Faff, 2013) between gender diversity and firm performance.
Results of both meta-analyses provide little evidence to support the business case for gender diversity. However, more importantly, a higher representation of females on corporate boards is also not associated with a detrimental effect on firm financial performance, which supports the ethical case for diversity. If increased female representation on corporate boards is not positively or negatively associated with firm performance, it seems reasonable to promote gender equality in board representation. Given the current underrepresentation of females on corporate boards in all studies included in our sample, and possibly in all countries worldwide (the largest average percentage of female directors included in our sample was
37% (Kyereboah-Coleman, 2006) and the overall average was only 14%), women should be prioritized for promotions if they are equally qualified. By bringing the performance-based
202 CHAPTER 6 and the ethical view together, fostering gender diversity in boardrooms seems justified and desirable.
We do, however, acknowledge that our univariate approach to this intricate research question is rather simplistic and might not do justice to the vast econometric complexity present when studying the relationship between gender diversity and firm performance.
Numerous other variables not investigated in this study might influence the relationship between female representation on corporate boards and firm financial performance. Future meta-analyses should investigate the relationship between various other diversity variables, such as age, tenure or education, on corporate boards and firm financial performance and subsume them in one analysis. Such an inclusive approach would yield benefits for practitioners and scientists.
The board’s limited influence on the firm financial performance might be a reason for the small overall effect size in the current meta-analysis. According to Bertrand and Schoar
(2003), the CEO and CFO only explain about 5 to 6% of variance in corporate performance measures. Hence, the boards’ effect on actual firm performance may be limited in general.
Various other factors which the board cannot alter or influence, such as the current economic and political situation, influence companies’ performance. Although such factors might be more important for firm performance than gender alone, they are difficult to quantify numerically for meta-analytic purposes.
When the Business Case Might Still Matter
The business case for diversity should not be abolished altogether. Whether an increased representation of females and the concomitant increase in gender diversity on corporate boards lead to performance benefits for the firm might depend on contextual factors, or on how diversity is managed. In our analysis, we aimed to find moderators of this relationship based on systematic differences between studies. Not surprisingly, the process of finding specific moderators was difficult, because there was high heterogeneity among studies in terms of reported study characteristics which could be used as potential moderators.
203 GENDER AND PERFORMANCE
The relationship between female representation and firm performance remained independent of how firm financial performance was measured. This supports our initial assumption that these outcome measures are relatively objective and measure firm financial performance similarly. In addition, this finding increases the certainty with which our results can be interpreted, because the non-significant relationship seems to be independent of the outcome measure. On a descriptive level, the results of this subgroup analysis are also in line with Post and Byron (2015), who find that accounting returns, such as ROA and ROE, increase and market performance (Tobin’s Q) remains unrelated to whether there are more female directors on corporate boards. While the significant positive relationship between accounting returns and female representation on corporate boards, which Post and Byron
(2015) find, deviates from our nonsignificant finding regarding these performance measures, their large sample size, which increased the power of their analysis and the likelihood of finding a significant result, is likely the reason for this deviation. Regardless of statistical significance, the overall magnitude of their effect size for ROA and ROE was similar to our effect sizes, and deducing practical implications from such small effect sizes might be debatable and misleading.
The characteristics of the country in which data were collected in the individual studies had little influence on the effect sizes in the current analysis. Neither a country’s development status, nor its GNI per capita influenced the relationship between female representation and firm financial performance. Thus, whether a country is considered developed/developing or “poor”/“rich” does not influence the effect that female board members have on firm performance. While previous studies have shown that a country’s characteristics, such as a long history of female participation in politics (Terjesen & Singh,
2008), influence the presence of women on corporate boards, our results suggest that a country’s characteristics related to economic performance do not influence the relationship between female representation and firm financial performance. This indicates that aiming for
204 CHAPTER 6 equality should guide decisions regarding future promotion to corporate boards, irrespectively of a country’s economic status.
In addition to our more rigorous meta-analytical approach to the research question, we also tested what Post and Byron (2015) called for: the moderating effect of board size on the relationship between female representation and firm performance. Larger boards might make it more difficult for directors to influence decisions and might limit the influence of directors on important decisions overall (Carpenter & Westphal, 2001; Westphal & Bednar, 2005), and the percentage of female directors on larger boards would therefore also matter less.
However, the number of directors on corporate boards did not significantly influence the effect size distribution in our analysis, suggesting that the non-significant relationship between female representation and firm performance remains similar, regardless of how many directors are on corporate boards, at least on the meta-analysis level. This is somewhat surprising because larger boards usually experience a higher complexity in all decision- making processes (Sanders & Carpenter, 1998) and have been shown to be associated with decreased financial performance (ROE; Conyon & Peck, 1998). The increased complexity on larger boards might make it even more difficult for females to have an impact, given their apparently wide underrepresentation. In conclusion, although a higher representation of females on boards does not appear to be directly associated with financial performance, more females on corporate boards might indirectly influence firms’ financial performance. For example, females might provide a protective effect against larger boards’ apparently increased interpersonal conflicts. However, this requires further research.
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Limitations and Future Research
There were a number of limitations in the current study. First, the main limitation is the small sample size of only 20 studies in our analysis, making it difficult to accept the null hypothesis due to low statistical power. However, overall, Post and Byron (2015) also report a similar result based on the higher number of studies in their analysis. Thus, it is unlikely that the apparent lack of a meaningful relationship between female representation on corporate boards and firm performance just resulted from low statistical power in both meta-analyses.
Interestingly, regardless of the studies’ high heterogeneity, the variability of the overall mean weighted effect sizes is reasonably consistent in our analysis. Specifically, as shown on
Figure 2, the effect sizes in all 20 studies can be classified as either positively or negatively small (most weighted correlation coefficients between ± .3). Thus, although it cannot be ruled out, it is unlikely that statistical power alone, or one or more hidden factors, consistently suppressed a relationship between female representation on boards and firm financial performance based on data from different countries and various industries. Future primary studies should attempt to focus on other diversity factors, such as educational level or the seniority of female board members of successful companies to determine how these characteristics affect firm performance.
Second, the quality of the included studies was not assessed by means of standardized scales. Instead, we only selected studies published in academic journals, assuming that such studies are of a higher quality than unpublished sources, because experts had reviewed them.
Furthermore, we also assumed that academic research on the topic might be more value-free than those in unpublished sources. Our assumption might not have been entirely correct, because the quality of the reported statistical data was generally poor in many of the examined studies. For example, although most studies conducted complex multivariate statistical analyses, it was often unclear if the reported regression coefficients were unstandardized or standardized (meaning that high-quality multivariate data corrected for various factors could not be used in our analysis). Therefore, the authors of future primary
206 CHAPTER 6 studies should use standardized guidelines to report quantitative results, which can be subsequently utilized in meta-analyses.
Third, including only published sources could potentially lead to a publication bias
(inflation of effect sizes) in a meta-analysis. However, based on the outcome of the various tests we conducted, there was little evidence of publication bias in our analysis. Interestingly, in the current analysis, the strongest evidence against publication bias is a simple visual inspection of the data in Figure 2. All the effect sizes in this figure are small, suggesting that the opposite of publication bias might have occurred in this analysis. That is, we might have failed to find studies with high magnitude effect sizes in the positive or negative direction.
Fourth, the current study relies on a linear model to determine the relationship between female representation and firm financial performance. It is possible that such a relationship depends on multiple factors in a non-linear fashion and that this changes over time. Thus, the linear assessment of data collected over a number of years might have contributed to the low effect sizes in the current meta-analysis. In general, it is difficult to determine the correct analytical approach to such a complex topic. Specifically, the relationship between female representation and firm financial performance was low, not only on a univariate study level, but also after the application of multivariate linear and non-linear approaches in the primary studies included in our analysis. Thus, the relationship between gender and financial performance might be truly negligible compared to other factors that might affect financial performance. Thus, future studies should focus on devising novel analytical approaches to study this topic.
Fifth, the data in our analysis come from countries with differing legal and board systems. Most pronounced is the difference between the one-tier and the two-tier board system, with various smaller differences between countries. In a one-tier board system, which is prevalent in the United States, the board is solely responsible for all corporate decisions.
Inside directors, who are directly employed in the company, represent the interests of the company’s stakeholders, while outside directors, who are usually employed in other
207 GENDER AND PERFORMANCE companies, bring a different perspective and objectivity to the boardroom. A two-tier board system, which, for example, is common in Germany, consists of an executive board and a supervisory board. The executive board manages the day-to-day business and the supervisory board supervises the executive board’s decisions. We did not investigate the influence of these factors, because there are too many minor differences between countries and they are too widespread to classify. While we do not expect these country differences to have a large influence on the current results, they might have contributed to the high heterogeneity in the studies’ effect sizes. Meta-analytically investigating the differing role that the diversity of internal and external directors might play in the boardroom with regards to firm performance could provide a future contribution to the scientific literature.
Sixth, two studies included in our sample were classified as statistical outliers due to their extreme effect sizes in opposite directions (Abdillahi & Manini, 2013; Wellalage &
Locke, 2013). The removal of these studies did not change the outcome of our analysis. These studies possibly were outliers, because both included a relatively small number of firms, nine and 88, respectively (compared to the mean number of firms of 146 in all the studies; see
Table 3). This might have increased the influence of individual firms and thereby skewed those studies’ results.
Seventh, it is possible that not all 20 studies were based on independent data, meaning that some firms might have contributed more data to the overall effect size than others. There was a possible overlap in the included firms from the same countries, because the primary studies did not rely on single firm data, but rather on data from business databases from the same country. For example, data from Norway were included in three studies in the current analysis (Ahern & Dittmar, 2012; Bøhren & Staubo, 2014; Dale-Olsen et al., 2013). To reduce this potential overlap in the data, we only once included data from the same time period, utilizing the same outcome measure in the same country.
Eighth, an overall higher representation of females might be needed in order to identify a relationship between diversity and performance. A shortcoming of the included data
208 CHAPTER 6 is that they are restricted in range, because the largest percentage of female directors included in our sample was 37% and the average was about 14%. This limits the meaningfulness of our findings, because few female directors are present in all the studies in general. This result supports the notion that more females should be promoted to director positions to meaningfully investigate the effects of gender diversity on performance. In accordance with this proposition, Joecks and colleagues (2012) suggest that a relatively low representation of females on boards first has a negative effect on firm performance (contradicting our findings), which only becomes positive after a critical mass of 30% female directors is reached. Thus, the current representation of 14% females on corporate boards in our sample might not be sufficient to show either the positive or negative effect that increased gender diversity might have on firm performance.
Lastly, women generally experience higher levels of chronic stress than men (Matud,
2004), especially when working in male-dominated industries, where they suffer from increased stress and worse mental health than men (Gardiner & Tiggemann, 1999) and might be perceived as tokens (Kanter, 1977). These differences in experienced stress might influence the relationship between gender diversity and firm performance, depending on the industry in which it is measured. For example, it would be interesting to examine if women’s influence is more pronounced in traditionally female-dominated industries as opposed to male-dominated industries. Similarly, Post and Byron (2015) also suggest that female directors might influence firm performance stronger in customer-proximal industries.
209 GENDER AND PERFORMANCE
Conclusion
In recent years, interest in gender diversity and the representation of females in leadership positions has evidenced a steep increase. Many scientific studies have investigated the relationship between female representation on corporate boards and firm financial performance, but, so far, the results are contradictory. The results of the current meta-analysis show that a higher representation of females on corporate boards is neither related to a decrease, nor to an increase in firm financial performance, confirming findings from a similar meta-analysis on this topic (Post & Byron, 2015). These results do not support the business case for diversity, which suggests that diversity is associated with an increase in performance.
However, they allow the conclusion that gender diversity should be promoted for ethical reasons to promote fairness. If a larger representation of female directors does not matter with regard to firm performance, females, if equally qualified, should be given priority when promotion decisions are made.
210
211 CHAPTER 7
CHAPTER 7 GENERAL DISCUSSION
212 GENERAL DISCUSSION
213 CHAPTER 7
The five empirical chapters presented in this dissertation deepen our understanding of individual differences in the prediction of behaviors and behavioral outcomes crucial for social and organizational functioning. While all chapters included in this dissertation aimed to achieve this broad goal, they differed with regard to the predictors and outcomes that were examined. Chapter 2 investigated how individual differences in SVO predict expectations of partner cooperation and cooperative behavior in social dilemmas, whereas these same individual differences in SVO were used to predict non-cooperative, norm-violating deviant behavior in Chapter 3. In Chapter 4, the focus shifted from the narrow personality facet SVO to broad personality domain scales by examining how these predict levels of workplace deviance. Chapter 5 investigated the effect of age on workplace deviance and examined personality changes and reductions in experienced negative affect across the lifespan as possible mediators. The last empirical chapter of this dissertation analyzed how another important individual difference (i.e., gender) relates to organizational performance. These empirical chapters share significant overlap and carry important overarching implications. In this final chapter, I will explore how the findings from these previous chapters relate to each other and discuss their broader implications. The remainder of this chapter is structured as follows: First, I will briefly summarize the main findings from the five empirical chapters included in this dissertation. Second, I will highlight the theoretical and practical implications of these findings. Third, I will develop ideas for future research. I will end with some conclusive remarks.
Overview of the Main Findings
Chapter 2: SVO, Expectations, and Cooperation
Chapter 2 provides a comprehensive meta-analytic overview of the relations between
SVO, expectations, and cooperation in social dilemmas. Beginning with the classic work by
Kelley and Stahelski (1970), researchers have been interested in the effect of dispositions on the formation of expected partner cooperation as fundamental building blocks of social cognition (Holmes, 2002). By integrating research from more than half a century, findings
214 GENERAL DISCUSSION demonstrated that prosocials expect significantly more cooperation from others than individualists and competitors, but that the latter two do not significantly differ in expected partner cooperation. These expectations partially mediate the relation between SVO and cooperation in social dilemmas. Importantly, this partial mediation holds for both prosocials and proselfs because expectations are associated with increased cooperation independently of an individual’s SVO. These findings carry important insights for the study of personality and trust, and for a wide variety of social behaviors, such as voting, recycling, donating to charities, or volunteering.
Chapter 3: SVO and Deviance
Individual differences in SVO do not just predict expectations and cooperation in social dilemmas, but as findings from Chapter 3 indicate also variance in deviant behavior.
Results from three studies (N = 556) demonstrated that SVO consistently relates to self- reported levels of workplace deviance and predicts responses on two behavioral measures of deviance. As such, proselfs were not just more likely than prosocials to indicate that they behave more deviantly at work, but they also disregarded instructions to a larger extent and were more dishonest about their performance to increase their own outcomes than prosocials.
Importantly, SVO as a narrow personality facet predicted incremental variance in workplace deviance over and above HEXACO Honesty-Humility. These findings suggest that SVO is a promising narrow personality facet for the prediction and prevention of deviant behavior.
Chapter 4: Personality and Workplace Deviance
Whereas the previous chapter examined how the narrow personality facet of SVO predicts deviant behavior, Chapter 4 examined the relations between the most commonly studied broad personality domain scales (Big Five and HEXACO) and workplace deviance.
Results from a meta-analytic integration of 460 effect sizes demonstrated that HEXACO
Honesty-Humility is the strongest predictor of workplace deviance out of all eleven broad personality domain scales that were examined (r = -.404). Conscientiousness (Big Five: r = -
.281; HEXACO: r = -.354), Agreeableness (Big Five: r = -.274; HEXACO: r = -.161),
215 CHAPTER 7
Neuroticism (Big Five: r = .142) or Emotionality (HEXACO: r = -.106), and Big Five
Openness to Experience (r = -.059) also significantly predict levels of workplace deviance.
Overall, the HEXACO domain scales (24.9%) explained more variance in workplace deviance than the Big Five domain scales (17.1%), and researchers and practitioners might therefore want to prioritize the HEXACO personality model when aiming to predict levels of workplace deviance.
Chapter 5: Age and Workplace Deviance
The fifth chapter of this dissertation provided a meta-analytic overview of the relation between age and workplace deviance (r = -.088, k = 135). Results demonstrate that this negative relation can be explained with socio-emotional selectivity theory (Carstensen, 1992) and with the neo-socioanalytical model of personality change (Roberts & Wood, 2006).
According to the former, individuals select into more positive and meaningful situations as they get older and should therefore experience less negative affect. As such, negative affect partially mediated the relation between age and workplace deviance. According to the latter, personality slightly changes across the adult lifespan (Roberts et al., 2006) and these age- related changes in Conscientiousness, Agreeableness, and Neuroticism also partially mediated the relation between age and workplace deviance. As far as I know, this meta-analysis is the first study that provides an empirical test of the underlying mechanisms for the relation between age and workplace deviance, and findings suggest that organizations could reap a competitive benefit by hiring more older employees when the reduction of deviant workplace behavior is of interest.
Chapter 6: Gender and Firm Performance
The last empirical chapter of this dissertation integrated contradictory findings about the relation between female representation on corporate boards and financial performance of organizations (e.g., Lückerath-Rovers, 2011; Mahadeo, Soobaroyen, & Hanuman, 2011;
Pathan & Faff, 2013; Van Ness, Miesing, & Kang, 2010), and thereby provided a test of the business case for diversity, which postulates that increased diversity will be associated with
216 GENERAL DISCUSSION performance benefits. Results demonstrated that female representation on corporate boards and firm financial performance do not correlate with each other (r = .01, k = 20). In other words, the increased representation of females on corporate boards is neither associated with a decrease or with an increase in firm financial performance. Although these results do not support the business case of gender diversity, they suggest that gender diversity on corporate boards should be promoted for ethical reasons: If female candidates are equally qualified as male candidates, females should be given priority in promotion decisions for corporate boards.
Theoretical Contributions and Implications
The findings of these five empirical chapters summarized above provide several theoretical contributions and implications that were discussed in the respective chapters. Here,
I want to explore the overarching, broad theoretical implications of these findings pertaining to research on personality as a predictor in organizational psychology. Importantly, the overall findings suggest that individual differences are useful in explaining differences in behavior between individuals. Except for the last empirical chapter of this dissertation, all chapters examined how personality relates to behavior, and three of these empirical chapters examined the relation between personality and workplace deviance. While Chapter 3 examined how the narrow personality facet of SVO relates to deviant behavior, Chapter 4 and 5 investigated the relation between the two most commonly used broad personality frameworks (i.e., Big Five and HEXACO) and workplace deviance. Overall, these results emphasize that personality is a strong predictor of workplace deviance (Berry et al., 2012, 2007; Salgado, 2002).
One theoretical implication of this dissertation for the study of personality pertains to the fact that narrow personality facets are useful when predicting workplace deviance
(Hastings & O’Neill, 2009). Results of Chapter 3, for the first time, demonstrate that the narrow facet of SVO significantly predicts self-reported and behavioral deviance, and that it explains incremental variance over and above HEXACO Honesty-Humility. In other words, the findings of this chapter introduce SVO as a useful narrow facet to the organizational
217 CHAPTER 7 psychology literature. Especially when the prediction of behaviors that influence multiple individuals is of interest, SVO promises to be a facet that deserves more attention as a predictor because it captures dispositional preferences in such interdependent situations. In addition, these results answer a call for more research about the predictive validity of personality facets that are not part of the Big Five personality model for workplace deviance
(O’Neill & Hastings, 2011).
However, not only narrow facets are useful when predicting organizational behavior.
Results of Chapter 4 provide valuable insights for studies in which broad personality domain scales are used to predict levels of workplace deviance. Notably, this chapter is the first to meta-analytically test the relations between the HEXACO domain scales and workplace deviance, and to compare it to the relations between the Big Five domain scales and workplace deviance. For the most part, the differences in correlations with workplace deviance between the two personality models under investigation were in the expected direction and therefore reflected conceptual differences between the two personality models.
Big Five Agreeableness correlated more strongly with workplace deviance than HEXACO
Agreeableness, most likely because Big Five Agreeableness captures variance associated with
HEXACO Honesty-Humility, which was the strongest predictor of workplace deviance. As expected, the relation between HEXACO Emotionality and workplace deviance was negative, probably because individuals scoring high on Emotionality combine high levels of anxiety and fearfulness with a strong need to form bonds with others. The relation between Big Five
Neuroticism and workplace deviance was positive, which suggests that the positive association of the anger facet with workplace deviance was stronger than the negative relation of the irritability and anxiety facets. Overall, these results support the conceptual differences between the Big Five and the HEXACO personality model, and thereby provide criterion- related validity for both.
Although personality is assumed to be a relatively stable individual difference, small changes across the adult lifespan do occur (Roberts et al., 2006). When examining the relation
218 GENERAL DISCUSSION between age and workplace deviance, results provided meta-analytic evidence for these small changes in personality and confirmed the prediction based on the neo-socioanalytical model of personality change that these personality changes mediate the negative relation between age and workplace deviance. The same holds for socio-emotional selectivity theory
(Carstensen, 1992), based on which it was hypothesized and found that reductions in experienced negative affect mediate the negative relation between age and workplace deviance. As such, results of this chapter also provide support for the neo-socioanalytical model of personality change and for the socio-emotional selectivity theory (Carstensen, 1992;
Roberts & Wood, 2006).
Practical Contributions and Implications
The findings of the five empirical chapters also highlight several contributions and implications to improve social, organizational, and societal functioning. Many of these implications have already been discussed in these chapters, but on the following pages I will highlight the two most important overarching practical implications. First, I will discuss practical implications pertaining to the promotion and advancement of social behaviors in various areas of life, such as when aiming to promote cooperative behavior between individuals or groups or when trying to avoid the occurrence of workplace deviance. Second,
I will discuss several practical implications for the use of individual differences in organizational behavior and in job selection situations. When discussing these implications, I will try to bridge the gap between the topics of the individual chapters and to use findings from one chapter to inspire implications for the area of research of another chapter.
The Promotion of Cooperative Behavior
Findings of Chapter 2 demonstrate that the relation between SVO and cooperation in social dilemmas is partially mediated by expectations of partner’s cooperation. One interpretation of this finding is that an individual’s own dispositions (SVO) are projected onto someone else (expectations), which, in turn, facilitates the influence of these dispositions on behavior (cooperation). Importantly, social projection is not limited to dispositional
219 CHAPTER 7 differences in cooperative preferences or in personality traits more generally, but can be applied to various perceptions about the self that are then projected onto others and subsequently result in a certain behavior. For example, social projection influences decision- making of financial brokers (Lee & Andrade, 2011), adolescent alcohol consumption (Marks,
Graham, & Hansen, 1992), or perceptions of political polarization (Van Boven, Judd, &
Sherman, 2012). Hence, the following implications might also apply to such diverse areas.
However, if the goal is to promote cooperative behavior between individuals and if cooperative dispositions are projected onto others, one important issue to address is how cooperative behavior can be promoted among those individuals who are not cooperatively predisposed. In other words, how can cooperative behavior be promoted among individuals with dispositional preferences for selfish behavior? One promising avenue would be to align the goals of social and selfish individuals to reduce the conflict of interest (Smith, 1979). For organizations, this could mean that performance goals are articulated on a team-level and are contingent on the successful achievement of everyone in the team. Such an alignment of goals for individuals with different cooperative dispositions, or an adoption of cooperative goals within teams (Tjosvold, Yu, & Hui, 2004), might increase intragroup cohesion and the occurrence of mutually helpful behavior (e.g., OCBs), decrease the occurrence of workplace deviance, and overall facilitate group performance by diminishing competitive behavior between individual employees. However, such structural changes to “payoff matrices” are often hard to accomplish or to implement in real life due to practical or political limitations, and in such instances the same measures that are then taken to promote cooperation in social dilemmas might also be useful in organizational settings: punishment for defection and rewards for cooperation (Balliet, Mulder, et al., 2011; Buckley et al., 1974). Such interventions might not only directly foster cooperative behavior between individuals, but might also affect the expectation that other individuals cooperate as well, which might exert another facilitating effect on cooperative behavior. One example of the implementation of these principles in the business world and in organizational psychology is transactional
220 GENERAL DISCUSSION leadership, according to which desired behavior should be rewarded and undesired behavior should be punished by leaders (Bono & Judge, 2004; Eagly et al., 2003).
Another practically important finding of Chapter 2 is that expectations and cooperative behavior are aligned for prosocials and for proselfs. In fact, this indicates that once the expectation arises that others cooperate, individuals are more likely to show cooperative behavior irrespectively of their cooperative dispositions. Whenever a certain desirable behavior needs to be promoted, messages or appeals should be framed in such a way that the expectation arises that other individuals are already behaving in the desired way. For example, when the occurrence of workplace deviance has reached problematic levels in a certain organization, it could be prevented by messages to employees that elicit the expectation that others are also refraining from acting deviantly (e.g., “90% of all employees did not act deviantly last week”) as opposed to highlighting the share of individuals who are acting deviantly (e.g., “10% of employees acted deviantly last week”). Fostering cooperative and social actions in such a way could be applied to various other desirable behaviors, such as volunteering, voting, recycling, making donations, or the promotion of organizational citizenship behavior.
Individual Differences in the Prediction of Organizational Behavior
Individual differences are often used to predict organizational behavior, and especially personality questionnaires are commonly used to select the most suitable applicants in job selections settings (Ones et al., 2007). The results of this dissertation highlight three important overarching implications for organizational behavior and job selection.
First, results of two chapters of this dissertation demonstrate that the two most often studied and most easily observable demographic characteristics – age and gender – either do not relate to the outcome (i.e., gender and organizational performance) or relate negatively to an undesired outcome (i.e., age and workplace deviance). Importantly, gender and age discrimination (i.e., sexism and ageism) are still common in hiring decisions (Ahmed et al.,
2012; Duncan & Loretto, 2004; Finkelstein et al., 1995; Finkelstein, King, & Voyles, 2015;
221 CHAPTER 7
Lamont, Swift, & Abrams, 2015), and the current results provide evidence indicating that discrimination should not just be actively prevented for ethical reasons, but also for performance-based reasons. In fact, based on the current results, older individuals and women
– two groups that are often discriminated against in the workplace – should be given priority in hiring and promotion decisions when equally qualified, especially when the goal is to decrease levels of workplace deviance and to foster gender equality in influential positions
(i.e., on corporate boards). In addition, increasing age is associated with a wide variety of other desirable outcomes, such as higher levels of organizational citizenship behavior or lower levels of tardiness and absenteeism (Ng & Feldman, 2008), and increased gender diversity can also result in a wide variety of desirable outcomes if managed correctly (e.g., van Knippenberg et al., 2004).
Second, results of Chapter 3, 4, and 5 demonstrate that personality is a strong predictor of workplace deviance. In fact, the HEXACO domain scales explain almost one quarter of the entire variance in workplace deviance. These findings can be used to advocate the use of personality questionnaires in job selection settings, especially for jobs in which problems with deviant employees have occurred in the past. The usefulness of personality questionnaires to predict workplace deviance is further highlighted by the fact that the prediction of behaviors such as workplace deviance or organizational citizenship behaviors enjoys an advantage over the prediction of core task performance: it is not limited to a specific job, but spans across tasks and work environments (Podsakoff et al., 2009). Thus, the current findings can be used to improve social and organizational functioning in basically all areas and organizations. However, the current results also suggest that practitioners interested in the prediction of applicants’ proneness to workplace deviance should not only rely on broad personality measures, but should also be aware of the benefits of narrow personality facets, such as SVO. Especially in job selection situations, relying on narrow facets is more efficient and might signal to applicants that the employed tests are relevant to the job (Ashton,
Paunonen, et al., 2014; Hastings & O’Neill, 2009).
222 GENERAL DISCUSSION
Third and finally, findings from all five empirical chapters can be interpreted in light of trait activation theory (Tett & Burnett, 2003). Trait activation theory argues that certain individual differences (e.g., personality traits) can be activated or inhibited by situational characteristics. In other words, individuals express certain personality traits more strongly when environmental influences activate these traits. This opens two interesting possibilities for the implementation of trait activation theory: 1) some environments might be better suited for individuals with certain traits, and 2) environments can be designed in such a way that the expression of certain desirable traits is facilitated.
Regarding the first point, organizations might hire individuals with different personality traits for different jobs. Whereas it is certainly desirable for all organizations to hire individuals who score high on Honesty-Humility, Conscientiousness, and Agreeableness, and who are prosocial when the avoidance of workplace deviance is of interest, other personality traits might be crucial depending on the job. Adjusting required personality profiles to the job in question will result in higher levels of person-job or person-organization fit (Caldwell & O’Reilly III, 1990), which, in turn, is associated with a wide variety of desirable outcomes (Kristof-Brown, Zimmerman, & Johnson, 2005). For example, for medical doctors it might be crucial to be highly conscientious, whereas someone working in marketing should score higher on Extraversion and Openness to Experience. Such a weighing of required personality profiles would certainly be beneficial for organizations.
Regarding the second point, work environments could be designed to facilitate the expression of certain desirable personality traits. For example, if organizations want to foster creative behavior, jobs could be designed more flexibly and communication between employees could be encouraged (Martens, 2011) to enhance the expression of traits related to, for example, Extraversion and Openness to Experience (Feist, 1998). Flexible working hours, open office spaces, or flat hierarchies could be facilitating factors for the expression of desired personality traits related to creative behavior.
223 CHAPTER 7
To summarize findings from all five empirical chapters with regard to the activated expression of desirable traits, societies and organizations would reap the greatest benefits if they create a climate that reduces age and gender discrimination, that encourages interdependent cooperation, and that facilitates the effects of desirable personality traits, such as Honesty-Humility, Conscientiousness, or Agreeableness, on behavior.
Directions for Future Research
Although the findings of this dissertation emphasize several opportunities to promote social, organizational, and societal functioning, combining the findings from these chapters highlights several open areas of research that could be addressed in the future. Findings from each chapter can mutually inspire future research ideas.
First, findings of Chapter 2 might suggest that prosocials project their own dispositions onto others, therefore expect higher levels of cooperation in interdependent situations, and subsequently are more likely to cooperate with others. In other words, these findings highlight the possibility that social projection is one underlying mechanism that can explain the relation between SVO and cooperation. Surprisingly, the underlying mechanisms linking personality to organizational behaviors, and especially to workplace deviance, have not been examined in much detail. One study found that the relation between
Conscientiousness and OCB is mediated by prosocial values, organizational concern, and impression management (Bourdage, Lee, Lee, & Shin, 2012), but other than that research is scarce in this area. Possibly, individuals scoring high on Honesty-Humility expect others to score high on Honesty-Humility as well (i.e., social projection of honest traits associated with high Honesty-Humility) and refrain from acting deviantly because they do not expect others to behave deviantly. A similar logic could be applied to all (anti-)social behaviors, and has already been shown for the relation between Honesty-Humility and trust, which is mediated by trustworthiness expectations (Pfattheicher & Böhm, 2017). Future research could examine the underlying mechanisms for the relation between personality and organizational behaviors,
224 GENERAL DISCUSSION and especially examine if a social projection account can explain these relations. Possibly, such a social projection process could occur differentially for different personality facets.
Second, Chapter 2 demonstrated that SVO predicts cooperation in social dilemmas, whereas Chapter 3 indicated that SVO predicts deviant behavior across different situations.
Taken together, these two findings suggest that cooperative behavior in social dilemmas might relate to deviant behavior. In other words, noncooperative behavior (i.e., defection) in a social dilemma might be conceptually similar to destructive deviant behavior at work. Future research could investigate if behavior in social dilemmas, or in economic games more generally, is predictive of workplace deviance or other organizational behaviors. If so, organizations could use economic games in job selection settings. While such serious games are already extensively studied and used for learning and educational purposes (Wouters, Van
Nimwegen, Van Oostendorp, & Van der Spek, 2013), the use of games in job selection is less prevalent. Future research could therefore develop serious games based on social dilemmas that might predict various organizational behaviors.
Third, the current findings highlight several opportunities for future studies at the interface of organizational and personality psychology. For example, the predictive validity of the Big Five and the HEXACO could be compared for a wide variety of outcomes. While the
HEXACO has received widespread attention as a predictor of workplace deviance (see
Chapter 4) and of cooperation in social dilemmas (Hilbig et al., 2016, 2013; Thielmann &
Hilbig, 2014), it has not been extensively studied as a predictor of other organizational behaviors. Doing so would shed light on the usefulness of the HEXACO in job selection settings, especially when not only the prediction of workplace deviance is of interest, but when personality questionnaires are used to predict a wide variety of organizational outcomes
(i.e., job performance, organizational citizenship behavior, work engagement). Another interesting avenue for future research would be to study the relation between personality facets and various organizational behaviors. For example, Hastings and O’Neill (2009) have shown that Big Five facets suppress each other (i.e., for Neuroticism) and that some facets
225 CHAPTER 7 almost explain as much variance in workplace deviance as the broad personality dimension they belong to. However, facet-level prediction of other organizational behaviors has largely been neglected and promises to be a fruitful avenue for future research given the possible benefits, such as increased efficiency and increased explained variance when predicting specific behaviors. Such research could be conducted using both the Big Five and the
HEXACO personality model when predicting a wide variety of organizational behaviors, and could possibly be even examined meta-analytically given the abundance of datasets that must exist. The finding that the narrow personality facet SVO predicts levels of workplace deviance also highlights the possibility that other personality facets that are not part of the broad personality models would be useful in the prediction of organizational behaviors as well. For example, risk-taking and seductiveness have already been shown to explain additional variance over and above the Big Five personality traits (O’Neill & Hastings, 2011).
In addition, it might be that certain personality domain scales interact with each other. For example, Extraversion moderates the relation between Honesty-Humility and workplace deviance, in a way that high levels of Extraversion strengthen this relation (Oh, Lee, Ashton,
& De Vries, 2011). Similar moderations might exist for other personality traits, and certain personality traits might have multiplicative effects on organizational behaviors.
Fourth, the interaction between personality traits and situational characteristics should be studied more extensively. In the introductory chapter of this dissertation, I provided several examples of how the outcome variables of interest (i.e., cooperation, deviance, performance) were affected by individual differences and situational characteristics. Although examining the effect of individual differences on these outcomes by themselves is promising and provides valuable insights for research and practice, investigating how individual differences and situational characteristics interact to determine behavior is crucial. The findings of the empirical chapters included in this dissertation highlight several such opportunities. For example, Zettler and Hilbig (2010) demonstrated that levels of workplace deviance are unaffected by situational characteristics among individuals scoring high on Honesty-Humility,
226 GENERAL DISCUSSION but change depending on situational characteristics for individuals scoring low on Honesty-
Humility. Future research could examine similar interactions for other personality traits and situational characteristics.
Fifth, in the introductory chapter, I explained the connection between the three main outcome variables under investigation in this dissertation (i.e., cooperation, deviance, performance) by suggesting that all three are determined by similar individual difference antecedents. In addition, I suggested that cooperative behavior might be facilitated through social norms, whereas workplace deviance is defined as a violation of norms. Organizational performance might be the results of the adherence to social and organizational norms and of both cooperative behavior between employees and the absence of workplace deviance.
Summarizing the findings from all five empirical chapters, one could propose an overarching model that starts with demographic characteristics influencing relatively stable personality traits. For example, Chapter 5 showed that personality changes slightly across the adult lifespan (Roberts et al., 2006), and gender differences in personality have also been observed
(Feingold, 1994). Other demographic characteristics, such as the number of siblings an individual has, might also exert small effects on personality. As a next step in the model, personality will influence certain behaviors, such as cooperation or workplace deviance, through different mechanisms (i.e., social projection). As mentioned above, these underlying mechanisms could be studied more elaborately. And ultimately, these behaviors might affect organizational performance. Taking a closer look at the two main behaviors investigated in this dissertation, it becomes apparent that the occurrence of workplace deviance has already been established as an important correlate of (organizational) performance (e.g., Dunlop &
Lee, 2004), while the effect of cooperative behavior between employees on organizational performance has received less scientific attention. Future research could examine such an overarching model.
227 CHAPTER 7
Concluding Remarks
The purpose of this dissertation was to examine the predictive validity of individual differences for three behaviors and behavioral outcomes crucial for social and organizational functioning – cooperation, deviance, and performance. Across five empirical chapters, the present dissertation shows that individual differences predict variations in behavior and therefore yields important implications for research and practice (even when findings were nonsignificant as in Chapter 6). Chapter 2 and 3 demonstrated that individual differences in the narrow personality facet SVO predict cooperative and deviant behavior, respectively.
Chapter 4 focused on the relations between broad personality domain scales and workplace deviance, and indicated that the HEXACO personality model explains more variance in workplace deviance than the Big Five personality model. Some of these personality domain scales (i.e., Conscientiousness, Agreeableness, Neuroticism) mediate the negative relation between age and workplace deviance (Chapter 5). Finally, results of Chapter 6 suggest that female representation on corporate boards is not related to organizational performance. Taken together, these five empirical chapters contribute to the facilitation of cooperative behavior, to the prediction and prevention of workplace deviance, and ultimately to the better understanding of a determinant of organizational performance. Taken together, these findings emphasize the utility of individual differences in the prediction of social and organizational behaviors.
In organizational and social psychology, the occurrence of unethical behavior, cheating, and norm violations is often viewed from the perspective that it is the power of the situation that leads people astray – for the moment or in enduring ways. Stanley Milgram and
Philip Zimbardo perhaps provided the strongest evidence for this, but more recent work by
Francesca Gino and Dan Ariely underlines this point. The present research shows that in new situations and in enduring organizational contexts, it is not just the power of the situation that leads people to violate norms – or to defect or to perform badly – but that personality matters.
This dissertation shows that broad and narrow personality traits and even global features of
228 GENERAL DISCUSSION individuals that are almost always salient in social life, such as age and gender, determine behaviors and behavioral outcomes crucial for social, organizational, and societal functioning.
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