moderator variable in research pdf

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Integrating Mediators and Moderators in Research Design

David p. mackinnon.

1 Department of Psychology, Arizona State University, Tempe, AZ, USA

The purpose of this article is to describe mediating variables and moderating variables and provide reasons for integrating them in outcome studies. Separate sections describe examples of moderating and mediating variables and the simplest statistical model for investigating each variable. The strengths and limitations of incorporating mediating and moderating variables in a research study are discussed as well as approaches to routinely including these variables in outcome research. The routine inclusion of mediating and moderating variables holds the promise of increasing the amount of information from outcome studies by generating practical information about interventions as well as testing theory. The primary focus is on mediating and moderating variables for intervention research but many issues apply to nonintervention research as well.

It is sufficiently obvious that both analysis and synthesis is necessary in classification and that both splitting and lumping have a place, or, to the extent that the terms involve antithesis, that neither one is correct. It is desirable that all distinguishable groups should be distinguished (although it is not necessary that all enter into formal classification and receive names). It is also desirable that they should all be gathered into larger units of increasing magnitude with grades, each of which has practical value and which are numerous enough to suggest degrees of affinity that can be and need to be specified. ( Simpson, 1945 , p. 23)

Two common questions in intervention outcome research are “How does the intervention work?” and “For which groups does the intervention work?” The first question is a question about mediating variables —variables that describe the process by which the intervention achieves its effects. The second question is about moderating variables —variables for which the intervention has a different effect at different values of the moderating variable. More information can be extracted from research studies if measures of mediating and moderating variables are included in the study design and data-collection plan. Furthermore, including measures of moderating and mediating variables is inexpensive, given their potential for providing information about how interventions work and for whom interventions work. Mediating and moderating variables are important for nonintervention outcome research as well as intervention research. A mediating variable is relevant whenever a researcher wants to understand the process by which two variables are related, such that one variable causes a mediating variable which then causes a dependent variable. Moderating variables are important whenever a researcher wants to assess whether two variables have the same relation across groups.

Third-Variable Effects

Mediating and moderating variables are examples of third variables. Most research focuses on the relation between two variables—an independent variable X and an outcome variable Y . Example statistics for two-variable effects are the correlation coefficient, odds ratio, and regression coefficient. With two variables, there are a limited number of possible causal relations between them: X causes Y , Y causes X , both X and Y are reciprocally related. With three variables, the number of possible relations among the variables increases substantially: X may cause the third variable Z and Z may cause Y ; Y may cause both X and Z , and the relation between X and Y may differ for each value of Z , along with others. Mediation and moderation are names given to two types of third-variable effects. If the third variable Z is intermediate in a causal sequence such that X causes Z and Z causes Y , then Z is a mediating variable; it is in a causal sequence X → Z → Y . If the relation between X and Y is different at different values of Z , then Z is a moderating variable. A primary distinction between mediating and moderating variables is that the mediating variable specifies a causal sequence in that a mediating variable transmits the causal effect of X to Y but the moderating variable does not specify a causal relation, only that the relation between X and Y differs across levels of Z . Diagrams for a mediating variable in Figure 1 and a moderating variable in Figure 2 demonstrate the difference between these two variables where the causal sequence is shown with directed arrows in Figure 1 to demonstrate a mediation relation. For moderation in Figure 2 , there is not an indirect relation of X to Y but there is an interaction XZ that corresponds to a potentially different X to Y relation at values of Z .

Single mediator model.

Single moderator model.

Another important third variable is the confounding variable that causes both X and Y such that failure to adjust for the confounding variable will confound or lead to incorrect conclusions about the relation of X to Y . A confounding variable differs from a mediating variable in that the confounding variable is not in a causal sequence but the confounding variable is related to both X and Y . A confounder differs from a moderating variable because the relation of X to Y may not differ across values of the confounding variable. Mediating and moderating variables are the focus of this article. More on these different types of third-variable effects are described elsewhere ( Greenland & Morgenstern, 2001 ; MacKinnon, 2008 ; MacKinnon, Krull, & Lockwood, 2000 ).

As you might expect, there are many more possible combinations of relations among four variables and as more variables are added, the number of possible relations among variables soon grows very complex. In this case with many variables, researchers typically often focus on third-variable effects such as moderating and mediating variables even in the most complex models. It is useful to remember that even though I focus on the simplest moderating and mediating model in this article, as the number of variables increases the number of possible models increases dramatically. Typically, the complexity of multivariable models is addressed with specific theoretical comparisons, specific types of variables, randomization, and specific tests based on prior research.

Mediating Variables

A single mediator model represents the addition of a third variable to an X → Y relation so that the causal sequence is modeled such that X causes the mediator, M , and M causes Y , that is, X → M → Y . Mediating variables are central to many fields because they are used to understand the process by which two variables are related. There are two overlapping ways in which mediating variables have been used in prior research: (a) mediation for design where interventions are designed to change a mediating variable and (b) mediation for explanation where mediators are selected after an effect of X to Y has been demonstrated to explain the mediating process by which X affects Y ( MacKinnon, 2008 , Chap. 2). The use of mediating variables for design is central to interventions designed to affect behavior. Intervention studies are based on theory for how the intervention is expected to change mediating variables and the change in the mediating variables is hypothesized to be what causes changes in an outcome variable. If the theory that the mediating constructs are causally related to the outcome is correct, then an intervention that changes the outcome will change the mediator. In an intervention to prevent sexually transmitted diseases, the intervention may be designed to change mediators of abstinence and condom use. In drug prevention research, mediating variables such as social norms, social competence skills, and expectations about drug use are targeted in order to change drug use. Many researchers have stressed the importance of assessing mediation in intervention research ( Baranowski, Anderson, & Carmack, 1998 ; Fraser & Galinsky, 2010 ; Judd & Kenny, 1981a , 1981b ; Kazdin, 2009 ; Kraemer, Wilson, Fairburn, & Agras, 2002 ; MacKinnon, 1994 ; Weiss, 1997 ).

The other major application of mediating variables is after an effect is observed and researchers investigate how this effect occurred. Mediation for explanation has a long history starting with the work of Lazarsfeld and others ( Hyman, 1955 ; Lazarsfeld, 1955 ) whereby observed relations between two variables are elaborated by considering a third variable and one explanation of why the two variables are related is because of mediation. Evaluating mediation to explain an observed effect is probably more susceptible to chance findings than evaluating mediation by design because the mediators in the mediation for design studies are selected before the study and mediators for explanation are usually selected after the study. Most programs of research employ both mediation by design and mediation for explanation approaches ( MacKinnon, 2008 , Chap. 2).

Reasons for including mediating variables in a research study

There are many overlapping reasons for including mediating variables in a research study. Seven reasons are listed below for the case of an intervention study as described elsewhere ( MacKinnon, 1994 , 2008 ; MacKinnon & Luecken, 2011 ).

• Manipulation check: Mediation analyses provide a check on whether the intervention produced a change in the mediating variables it was designed to change (e.g., if the intervention was designed to engender an antitobacco norm, then program effects on norms should be observed). If the program did not change the mediating variable, it is unlikely to have the desired effects on the targeted outcome. Failure to significantly change the mediator may occur because the intervention was unsuccessful, the measurement of the mediating variable was inadequate, or by chance statistical fluctuations.
• Program improvement: Mediation analyses generate information to identify successful and unsuccessful intervention components. If an intervention component did not change the mediating variable, then the actions selected to change the mediating variable need improvement. For example, if no program effects on social norms are found, the intervention may need to reconsider the intervention components used to change norms. If the program increases norms but norms do not affect the outcome, the norms component of the program may be ineffective and/or unnecessary and new mediators may need to be included.
• Measurement improvement: A lack of an intervention effect on a mediator can suggest that the measures of the mediator were not reliable or valid enough to detect changes. If no program effects are found on norms, for example, it may be that the method used to measure norms is not reliable or valid.
• Possibility of delayed program effects: If the intervention does not have the desired effect on the outcome variable but does significantly affect theorized mediating variables, it is possible that effects on outcomes will emerge later after the effects of the mediating variable have accumulated over time. For example, the effects of a norm change intervention to reduce smoking onset among young children may not be evident until the children are older and have more opportunities to smoke.
• Evaluating the process of change: Mediation analysis provides information on the processes by which the intervention achieved its effects on an outcome measure. For example, it is possible to study whether the changes in mediators like norms or another mediator were responsible for intervention effects on smoking.
• Building and refining theory: One of the greatest strengths of including mediating variables is the ability to test the theories upon which intervention programs were based. Many theories are based on results of cross-sectional relations with little or no randomized experimental manipulation. Mediation analysis in the randomized design is ideal for testing theories because it improves causal inference. Competing theories for smoking onset and addiction, for example, may suggest alternative mediating variables that can be tested in an experimental design.
• Practical implications: The majority of intervention programs have limited resources to accomplish their goals. Intervention programs will cost less and provide greater benefits if the critical ingredients of interventions can be identified because critical components can be retained and ineffective components removed. Mediation analyses can help decide whether to discontinue an intervention approach or not by providing information about whether it was a failure of the intervention to change the mediator, called action theory or whether it was a failure of a significant relation of the mediator to the outcome, called conceptual theory, or both.

How to include mediating variables in a research study

There are two major aspects to adding mediating variables to a research study. First is during the planning stage where the theoretical framework of the study and testing theory is considered and often specified in a logic model. In many cases, the development of a logic model may take considerable time and resources because it forces researchers to carefully consider how the intervention components could be reasonably expected to change an outcome. In fact, the most important aspect of considering mediating variables in a research study may be that it forces researchers to think in a concrete manner about how the intervention could be expected to work both in terms of action theory for how the intervention affects the mediators and conceptual theory for which mediators are related to the outcome. The second aspect to adding mediating variables is deciding how to measure theoretical mediating variables. Typically, this requires adding measures to a questionnaire or some other measurement procedure. In many cases, there may not be existing measures of relevant mediating constructs and psychometric work must be done to develop measures of mediating variables. Furthermore, the addition of measures of mediating variables can add to the respondent burden on a questionnaire. Nevertheless, the addition of mediating variable measures may generate practical and theoretical information from a research study. It is important to measure mediating variables in both intervention and control conditions before and after the intervention to ascertain change in the measures and for statistical mediation analysis.

Mediation Regression Equations

The ideas regarding mediating variables can be translated into equations suitable for estimating mediated effects and conducting statistical tests as for the single mediator model for X, M , and Y shown in Figure 1 and defined in Equations 2 and 3 below. Equation 1 is also shown because it provides information for mediation relations and corresponds to the overall intervention effect:

Where X is the independent variable, Y is the outcome variable, and M is the mediating variable; the parameters i 1 , i 2 , and i 3 are intercepts in each equation; and e 1 , e 2 , and e 3 are residuals. In Equation 1 , the coefficient c represents the total effect, that is, the total effect that X can have on Y , the outcome variable. In Equation 2 , the parameter c’ denotes the relation between X and Y controlling for M , representing the direct effect—the effect of X on Y that is adjusted for M , the parameter b denotes the relation between M and Y adjusted for X . Finally, in Equation 3 , the coefficient a denotes the relation between X and M . Equations 2 and 3 are represented in Figure 1 , which shows how the total effect of X on Y is separated into a direct effect relating X to Y and a mediated effect by which X indirectly affects Y through M . Complete mediation is the case where the total effect is completely explained by the mediator, that is, there is no direct effect. In this case, the total effect is equal to the mediated effect (i.e., c = ab ). Partial mediation is the case where the relation between the independent and the outcome variable is not completely accounted for by the mediating variable. There are alternative estimators of the mediated effect including difference in coefficients and product of coefficients, which are based on the regression equations. More on the different approaches to mediation analysis can be found elsewhere including standard errors, confidence limit estimation, multiple mediators, qualitative methods, experimental designs, limitations for causal inference, and categorical outcomes ( MacKinnon, 2008 ).

Assumptions of the Single Mediator Model

Although statistical mediation analysis is straightforward under the assumption that the mediation equations above are correctly specified, the identification of true mediating variables is complicated by several testable and untestable assumptions ( MacKinnon, 2008 ). New developments in mediation analysis address statistical and inferential assumptions of the mediation model. For the estimator of the mediated effect, simultaneous regression analysis assumptions are required, such as the residuals in Equations 2 and 3 are independent and that M and the residual in Equation 2 are independent ( MacKinnon, 2008 ; McDonald, 1997 ). No XM interaction in Equation 2 is assumed, although this can be tested statistically. The temporal order of the variables in the model is also assumed to be correctly specified (see Cheong, MacKinnon, & Khoo, 2003 ; Cole & Maxwell, 2003 ; MacKinnon, 2008 ). The methods assume a self-contained model such that no variables related to the variables in the mediation equations are omitted from the estimated model and that coefficients estimate causal effects ( Holland, 1988 ; Imai, Keele, & Tingley, 2010 ; Lynch, Cary, Gallop, & Ten Have, 2008 ; Ten Have et al., 2007 ; VanderWeele, 2010 ). It is also assumed that the model has minimal errors of measurement ( James & Brett, 1984 ; McDonald, 1997 ).

Moderating Variables

The strength and form of a relation between two variables may depend on the value of a moderating variable. A moderator is a variable that modifies the form or strength of the relation between an independent and a dependent variable. The examination of moderator effects has a long and important history in a variety of research areas ( Aguinis, 2004 ; Aiken & West, 1991 ). Moderator effects are also called interactions because the third variable interacts with the relation between two other variables. However, theoretically moderator effects differ slightly from interaction effects in that moderators refer to variables that alter an observed relation in the population while interaction effects refer to any situation in which the effect of one variable depends on the level of another variable. As mentioned earlier, the moderator is not part of a causal sequence but qualifies the relation between X and Y . For intervention research, moderator variables may reflect subgroups of persons for which the intervention is more or less effective than for other groups. In general, moderator variables are critical for understanding the generalizability of a research finding to subgroups.

The moderator variable can be a continuous or categorical variable, although interpretation of a categorical moderator is usually easier than a continuous moderator. A moderating variable may be a factor in a randomized manipulation, representing random assignment to levels of the factor. For example, participants may be randomly assigned to a moderator of treatment dosage in addition to type of treatment received in order to test the moderator effect of duration of treatment across the two treatments. Moderator variables can be stable aspects of individuals such as sex, race, age, ethnicity, genetic predispositions, and so on. Moderator variables may also be variables that may not change during the period of a research study, such as socioeconomic status, risk-taking tendency, prior health care utilization, impulsivity, and intelligence. Moderator variables may also be environmental contexts such as type of school and geographic location. Moderator variables may also be baseline measure of an outcome or mediating measure such that intervention effects depend on the starting point for each participant. The values of the moderating variable may be latent such as classes of individuals formed by analysis of repeated measures from participants. The important aspect is that the relation between two variables X and Y depends on the value of the moderator variable, although the type of moderator variable, randomized or not, stable characteristic, or changing characteristic often affects interpretation of a moderation analysis. Moderator variables may be specified before a study as a test of theory or they may be investigated after the study in an exploratory search for different relations across subgroups. Although single moderators are described here referring to the situation where the relation between two variables differs across the levels of a third variable, higher-way interactions involving more than one moderator are also possible.

Reasons for including moderating variables in a research study

There are several overlapping reasons for including moderating variables in a research study.

• Acknowledgment of the complexity of behavior: The investigation of moderating variables acknowledges the complexity of behavior, experiences, and relationships. Individuals are not the same. It would be unusual if there were no differences across individuals. This focus on individual versus group effects is more commonly known as the tendency for researchers to be either lumpers or splitters ( Simpson, 1945 ). Lumpers seek to group individuals and focus on how persons are the same. Splitters, in contrast, look for differences among groups. By making this distinction, I guess I am a splitter. Generally, the search for moderators is a goal of splitters while lumpers would tend not to focus on moderator variables but on general results across all persons. Of course both approaches have problems with splitters yielding smaller and smaller groups until there is one person in each group. Lumpers will fail to observe real subgroups, including subgroups that may have iatrogenic effects or miss predictive relations because of opposite effects in subgroups.
• Manipulation check: If there is an additional experimental factor picked so that an observed relation is differentially observed across subgroups, then the intervention effect is a test of the intervention theory. For example, if dose of an intervention is manipulated as well as intervention or control, then the additional dosages could be considered a moderator and if the intervention effect is successful, the size of the effect should differ across levels of dosage.
• Generalizability of results: Moderation analysis provides a way to test whether an intervention has similar effects across groups. It would be important, for example, to demonstrate that intervention effects are obtained for males and females if the program would be disseminated to a whole group containing males and females. Similarly, the consistency of an intervention effect across subgroups demonstrates important information about the generalizability of an intervention.
• Specificity of effects: In contrast to generalizability, it is important to identify groups for which an intervention has its greatest effects or no effects. This information could then be used to target groups for intervention thereby tailoring of an intervention.
• Identification of iatrogenic effects in subgroups: Moderation analysis can be used to identify subgroups for which effects are counterproductive. It is possible that there will be subgroups for which the intervention causes more negative outcomes.
• Investigation of lack of an intervention effect: If there are two groups that are affected by an intervention in opposite ways, the overall effect will be nonsignificant even if there is a statistically significant intervention effect in both groups, albeit opposite. Without investigation of moderating variables, these types of effects would not be observable. In addition, before abandoning an intervention or area of research it is useful to investigate subgroups for any intervention effect. Of course, this type of exploratory search must consider the possibility of multiplicity where by testing more effects will lead to finding effects by chance alone.
• Moderators as a test of theory: There are situations where intervention effects may be theoretically expected in one group and not another. For example, there may be different social tobacco intervention effects for boys versus girls because reasons for smoking may differ across sex. In this way, mediation and moderation may be combined if it is expected that a theoretical mediating process would be present in one group but not in another group.
• Measurement improvement: Lack of a moderating variable effect may be due to poor measurement of the moderator variable. Many moderator variables have reasonably good reliability such as age, sex, and ethnicity but others may have measurement limitations such as risk-taking propensity or impulsivity.
• Practical implications: If moderator effects are found, then decisions about intervention delivery may depend on this information. If intervention effects are positive at all levels of the moderator, then it is reasonable to deliver the whole program. If intervention effects are observed for one group and not another, it may be useful to deliver the program to the group where it had success and develop a new intervention for other groups. Of course, there are cases where the delivery of an intervention as a function of a moderating variable cannot be realistically or ethically used in the delivery of an intervention. For example, it may be realistic to deliver different programs to different ages and sexes but less realistic to deliver programs based on risk taking, impulsivity, or prior drug use, for example, because of labeling of individuals or practical issues in identifying groups. By grouping persons for intervention, there may also be iatrogenic effects, for example, grouping adolescent drug users together may have harmful effects by enhancing a social norm to take drugs in this group.

How to include moderators in a research study

Moderators such as age, sex, and race are often routinely included in surveys. Demographic characteristics are also often measured including family income, marital status, number of siblings, and so on. Other measures of potential moderators have the same measurement and time demand issues as for mediating variables described earlier; that is, additional measures may increase respondent burden.

Moderation Regression Equations

The single moderating variable effect model is shown in Figure 2 and summarized in Equation 4 .

Where Y is the dependent variable, X is the independent variable, Z is the moderator variable, and XZ is the interaction of the moderator and the independent variable; e 1 is a residual, and c 1 , c 2 , and c 3 represent the relation between the dependent variable and the independent variable, moderator variable, and moderator by independent variable interaction, respectively. The moderating variable XZ is the product of X and Z where X and Z are often centered (centered means that the average is subtracted from each observed value of the variable). If the XZ interaction is statistically significant, the source of the significant interaction is often explored by examining conditional effects with contrasts and plots. More on interaction effects including procedures to plot interactions and test contrasts can be found in Aguinis (2004) , Aiken and West (1991) , Keppel and Wickens (2004) , and Rothman, Greenland, and Lash (2008) .

Assumptions of Moderation Analysis

There are several challenges to accurate identification of moderator effects. For example, interactions are often scale dependent so that changing the measurement scale can remove an interaction effect. The range of values of the moderator may affect whether a moderator effect can be detected. The number of moderators tested may increase the chance of finding a Type I error and the splitting of the total sample into subgroups limits power to detect moderator effects. Several types of interaction effects are possible that reflect different conclusions, for example, an intervention effect may be statistically significant and beneficial in each group but the effect may differ, an intervention effect may be statistically significant in one group but not another, and so on. More on these issues can be found in Aiken and West (1991) and Rothman et al. (2008) and a special issue on subgroup analysis in a forthcoming issue of the journal Prevention Science .

Moderation and Mediation in the Same Analysis

Both moderating and mediating variables can be investigated in the same research project but the interpretation of mediation in the presence of moderation can be complex statistically and conceptually ( Baron & Kenny, 1986 ; Edwards & Lambert, 2007 ; Fairchild & MacKinnon, 2009 ; Hayduk & Wonnacott, 1980 ; James & Brett, 1984 ; Preacher, Rucker, & Hayes, 2007 ). There are two major types of effects that combine moderation and mediation as described in the literature ( Baron & Kenny, 1986 ; Fairchild & MacKinnon, 2009 ): (a) moderation of a mediation effect , where the mediated effect is different at different values of a moderator and (b) mediation of a moderation effect , where the effect of an interaction on a dependent variable is mediated.

An example of moderation of a mediation effect is a case where a mediation process differs for males and females. For example, a program may affect social norms equally for both males and females but social norms only significantly reduce subsequent tobacco use for females not for males. These types of analyses can be used to test homogeneity of action theory across groups and homogeneity of conceptual theory across groups ( MacKinnon, 2008 ). An example of moderation of a mediated effect is a case where social norms mediate the effect of an intervention on drug use but the size of the mediated effect differs as a function of risk-taking propensity. An example of mediation of a moderator effect would occur if the effect of an intervention depends on baseline risk-taking propensity such that the interaction is due to a mediating variable of social norms, which then affects drug use. These types of effects are important because they help specify types of subgroups for whom mediational processes differ and help quantify more complicated hypotheses about mediation and moderation relations. Despite the potential for moderation of a mediation effect and mediation of a moderation effect, few research studies include both mediation and moderation, at least in part because of the difficulty of specifying and interpreting these models. General models that include mediation and moderation have been described that include the single mediator model as a special case and the single moderator model as special cases ( Fairchild & MacKinnon, 2009 ; MacKinnon, 2008 ).

Both mediating variables and moderating variables are ideally specified before the study is conducted. Describing mediation and moderation theory clarifies the purpose of the intervention and forces consideration of alternative interpretations of the results of the study leading to better research design and more information gleaned from the study. Stable characteristic moderator variables such as age and sex are often routinely included in research studies. Often existing studies include some measures of moderating and mediating variables so that mediation and moderation analysis of many existing data sets can be conducted. More information can be obtained from these studies if mediation and moderation analyses are conducted.

There are some limitations of including moderating and mediating variables. First, there is the cost and time spent developing mediation and moderation theory prior to a study. It is likely that consideration of these variables may alter the entire design of a study possibly delaying an important research project. However, it is likely that interventions will be more successful if based on theory and prior research and the application of these analyses inform the next intervention study. Second, there are substantial conceptual and statistical challenges to identifying true moderating and mediating variables that require a program of research. The identification of true mediating processes, for example, requires a program of research with information from many sources. Third, the questions added to a survey to measure mediating and moderating variables must be balanced with the quality of data collected. A longer survey that bores participants or renders some or all of their data inaccurate will not help a research project. Nevertheless, the addition of mediating and moderating variables to any research program reflects the maturation of scientific research to addressing the specifics of how and for whom interventions achieve their effects.

Acknowledgments

The author disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported in part by Public Health Service Grant DA0957.

This article was previously presented at the Stockholm Conference on Outcome Studies of Social, Behavioral, and Educational Interventions, on February 7, 2011. It was invited and accepted at the discretion of the editor.

Declaration of Conflicting Interests

The author declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

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Moderating and mediating variables in psychological research

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Statistical mediation and moderation have most prominently been distinguished by Baron and Kenny (1986). More complex models that combine both of these effects have recently received increased attention, namely mediated moderation and moderated mediation (e.g., Muller, Judd, & Yzerbyt, 2005). Presently the focus is on a three variable model that is often claimed to represent an instance of moderated mediation or mediated moderation. More specifically, in this model a single variable is considered to simultaneously mediate and moderate the same effect. We show that this specific model however cannot be considered either an instance of mediated moderation nor of moderated mediation. Also, we argue that this particular model is a priori misspecified. A data pattern that seems to agree with this model is recognized as plausible, but it indicates that the model must be modified in one of two ways to be methodologically sound. We conclude by recommending to not use this three variable model and to consider evidence that seemingly agrees with it as evidence that the three variable model is inadequate.

As more complex statistical analyses become accessible through modern computer software packages, so the terms “Mediation”, “Moderation” and “(Statistical) Interaction” see increased use. At the same time, uncertainty continues over their definitions and discrimination as evident in inconsistent guidelines and varying means of testing. Further, while guideline papers continue to be written, to the best of our knowledge none has yet been written specifically for Educational Researchers. Here we address this discrepancy with a provision of clear definitions that discriminate, note real-life ambiguities particular to educational research, and cover the various means of testing that are available for researchers. The paper ends with the provision of an example Moderation from educational research which is tested with three alternative statistical approaches, the results of which are then compared and contrasted.

Annual Review of Psychology

David P Mackinnon

Learning and Individual Differences

Fabrice Dosseville

Nabhan Sanusi

JeeWon Cheong , Angela Pirlott , David P Mackinnon

Muhammad Hafizh

Learning is the process of developing the attitude and personality of the students through various stages and experiences. In the achievement of goals requires methods and media as a tool to explain the subject in developing the attitude and personality of the student. The method used should be in accordance with the subject matter taught. Teachers can do a combination of methods and complement with certain media including music. Utilization of music as a medium of learning that makes the learning process fun and not boring. Music can balance the intellectual and emotional intelligence so that it will provide good results for students. In addition music also affects physiological conditions. Relaxing physiological conditions will inspire students to follow the learning process. Relaxation accompanied by music keeps the mind ready and able to concentrate more on receiving lessons. The most helpful music in the learning process is baroque music. Baroque music uses distinctive taps and patterns that automatically synchronize students' bodies and minds. In addition there is a classical music that is said to be able to balance between the right brain with the left brain or commonly called the intellectual intelligence with the emotional students. Students who have received music education from an early age, adults will become human beings who have logical thinking, intelligent, creative, able to make decisions and possess empathy.

Behavior Research Methods

The effect of justice sensitivity on malevolent creativity: the mediating role of anger and the moderating role of emotion regulation strategies

• Yan Wang 1   na1 ,
• Keke Zhang 1   na1 ,
• Fangfang Xu 1 ,
• Yayi Zong 1 ,
• Lujia Chen 1 &
• Wenfu Li 1  

BMC Psychology volume  12 , Article number:  265 ( 2024 ) Cite this article

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Metrics details

the AMORAL model emphasizes the close connection of individuals’ belief system and malevolent creativity. Belief in a just world theory (BJW) states that people have a basic need to believe that the world they live in is just, and everyone gets what they deserve. Therefore, justice matters to all people. Justice sensitivity, as one of individual trait, has been found associated with negative goals. However, relevant studies have not tested whether justice sensitivity can affect malevolent creativity and its psychological mechanisms. Additionally, researchers have found that both anger and emotion regulation linked with justice sensitivity and malevolent creativity, but their contribution to the relationship between justice sensitivity and malevolent creativity remained unclear. The current study aims to explore the influence of justice sensitivity on malevolent creativity, the mediating effect of trait anger/state anger on the relationship between justice sensitivity and malevolent creativity, and the moderating effect of emotion regulation on this mediating effect.

A moderated mediating model was constructed to test the relationship between justice sensitivity and malevolent creativity. A sample of 395 Chinese college students were enrolled to complete the questionnaire survey.

Justice sensitivity positively correlated with malevolent creativity, both trait anger and state anger partly mediated the connection between justice sensitivity and malevolent creativity. Moreover, emotion regulation moderated the indirect effect of the mediation model. The indirect effect of justice sensitivity on malevolent creativity through trait anger/state anger increased as the level of emotion regulation increased. The results indicated that justice sensitivity can affect malevolent creativity directly and indirectly through the anger. The level of emotion regulation differentiated the indirect paths of justice sensitivity on malevolent creativity.

Conclusions

Justice sensitivity and malevolent creativity was mediated by trait anger/state anger. The higher sensitivity to justice, the higher level of trait anger/state anger, which in turn boosted the tendency of malevolent creativity. This indirect connection was moderated by emotion regulation, individuals with high emotion regulation are better able to buffer anger from justice sensitivity.

Peer Review reports

Creativity is generally believed that it is beneficial and positive. Researchers pointed out that creativity refers to the ability to generate novel and appropriate ideas or products in a specific environment [ 1 ]. However, creativity is not always positive. People also have some novel but negative ideas and behaviors—malevolent creativity, which refers to original and premeditated ideation deliberately performed in order to realize one’s own goals and desires, and it always leads to negative consequences, such as new types of fraud, murder, etc [ 2 ]. A wide variety of malevolent creativity instances can be found everywhere and cause damage in original or innovative ways, and it is hard to detect and prevent [ 3 ]. Therefore, it is of great social significance to reveal the influence factors of malevolent creativity and explore the effective regulation strategies to reduce the potential harm.

The AMORAL model emphasized individuals’ belief system, or their interconnected set of beliefs helped determine whether and to what extent they engage in malevolent creativity. Moreover, the drivers of malevolent creativity also included the need to align actions with belief systems [ 4 ]. At the same time, belief in a just world theory (BJW) stated that people had a basic need to believe that the world they lived in is just, and everyone got what they deserved [ 5 , 6 ]. Researchers had found that individuals were more frequently exhibit malevolent creativity in hostile, angry, injustice, and vengeful situation [ 7 ]. Meanwhile, social exchange theory stated that justice was the basis of social exchange and an essential element of effective social interaction. Injustice in an organization or group was a source of stress for its members which were provoked into negative emotions and even outright antisocial aggression by differential treatment [ 8 ]. Therefore, the feeling of injustice may matter to malevolent creativity.

A variety of studies examining the distributive, interpersonal, and procedural justice showed that it was perceived justice, not objective circumstances, shaped responses to injustice [ 9 ]. Justice sensitivity is an individual trait, which is reflected in the difficulty of detecting injustices and the intensity of the response to injustices. Individuals with high justice sensitivity are more likely to perceive injustice than those with low justice sensitivity [ 10 ]. Schmitt et al. categorized justice sensitivity into four types: victim sensitivity, observer sensitivity, beneficiary sensitivity and perpetrator sensitivity [ 11 ]. Mohiyeddini and Schmitt found that justice sensitivity performed better than other variables (e.g., trait anger, anger out, and self-assertiveness) in predicting reactions to unfair treatment [ 12 ]. There was a study found that individuals tend to establish negative goals when they encountered unfair situations, which may lead to the emergence of malevolent creativity [ 13 ]. Another studies also showed that justice sensitivity closely positively correlated kinds of externalizing problems, such as relational, proactive, and reactive aggression in adults [ 14 ] and peer victimization [ 15 ]. What’s more, Gollwitzer et al. found victim sensitivity was associated negatively with prosocial behavior and positively with antisocial behavior [ 16 ]. Prior studies verified that people who have encountered an injustice situation would show more malevolent creativity. However, it remains unknown whether justice sensitivity can affect malevolent creativity and how it affects malevolent creativity. Therefore, the current study focused on the influence of justice sensitivity on malevolent creativity and explored the underlying mechanisms.

Anger is a basic emotional state, according to State-Trait Anger theory, which can be divided into state anger and trait anger [ 17 ]. State anger is a temporary emotional state which composed of subjective feelings and physiological activities. On the other hand, trait anger is defined as a stable personality characteristic, a general tendency of angry reaction under the induced stimulus, and a relatively stable individual difference in frequency, intensity and duration of state anger [ 18 ]. High-trait angry individuals are more inflamed and easily develop state anger, then show more maladaptive cope including verbal and physical confrontation [ 19 , 20 ].

Equity theory stated that negative emotions such as anger and resentment were aroused when individuals realized they had been treated unfairly [ 21 ]. Social psychological researches indicated that anger was the predominant emotional response to perceiving injustice [ 22 , 23 ]. A number of empirical studies also examined the relationship between anger and injustice, and indicated that the level of anger was higher when individuals perceived injustice or had been treated unfairly [ 24 , 25 , 26 , 27 ]. Furthermore, researches showed that facets of anger (i.e., state, trait, expression, inhibition) linked with perceived injustice [ 28 , 29 ]. Schmitt et al. also found justice sensitivity related with trait anger [ 30 ]. Additionally, individuals with high justice sensitivity may be more likely to have a stronger reaction when they accounted injustice events, which might in turn produce a higher degree of state anger.

It has been shown that feeling unfair treatment can give people a sense of relative deprivation [ 31 ], which lead to anger and criminal behavior [ 10 , 32 , 33 ]. Anger was an emotion with high arousal and approach orientation which could reinforce cognitive activation state, and allowed the person to mobilize more adequate cognitive resources to engage in the current cognitive activity (e.g., creative thinking). Therefore, anger could facilitate creative performance [ 34 , 35 , 36 , 37 ]. Cheng et al. conducted an experimental study with the malevolent creativity task (MCT) and found that malevolent creativity performance can be significantly promoted in anger group [ 38 ]. Therefore, anger may be a potential mediating variable between justice sensitivity and malevolent creativity.

Previous studies explored induced anger emotion in the laboratory, but few researches examined the relationship between anger and malevolent creativity under natural conditions. There was a study shown that trait anger could significantly and positively predicted aggression [ 39 ]. And other study also found that state anger could influence an individual’s tendency to aggression through anger rumination [ 40 ]. Thus, the current study speculated that both trait anger and state anger may influence the tendency to malevolent creativity. Additionally, whether trait anger and state anger play a different role between justice sensitivity and malevolent creativity is still unknown. Therefore, both state anger and trait anger deserve attention. Considering the differences between the two kinds of anger, the current study separately examined their roles between justice sensitivity and malevolent creativity.

However, in realistic situations, justice-sensitive individuals do not always produce extreme anger emotion and generate tendency to malevolent creative behavior when they faced with injustice events. This may closely rely on the regulation and control of emotion production, perception, and expression.

Emotion regulation, composed of cognitive reappraisal and expressive suppression, is defined as a series of cognitive processes adjusting or changing the appearance, intensity and duration of emotion [ 41 ]. Cognitive behavioural therapy (CBT) approaches proved that angry emotion can be best downregulated by those emotion regulation strategies, such as modifying negative thoughts, or reappraising the anger-provoking situation [ 42 ]. Furthermore, according to Gross’s process model of emotion regulation, strategies that act early in the emotion-generative process might differ from the later one in consequences [ 43 ]. Cognitive reappraisal is an antecedent-focused strategy used before an emotion occurs, individual can change the emotional experience by altering the perception of a negative event. Expressive suppression, on the other hand, refers to an individual’s ability to alter the external manifestation of emotion by inhibiting expression. That is to say, both can work in the early stages of emotion production. Researchers examined the effects of emotion regulation strategies on both trait anger and state anger, andresults showed that both cognitive reappraisal and emotion suppression can counteract short-term anger arousal following provocation [ 42 ]. Numerous studies showed that high level emotion regulation could effectively down-regulate an individual’s anger mood and the related physiological responses [ 42 , 44 , 45 , 46 ]. Cheng et al. also found that cognitive reappraisal and expressive suppression could effectively reduce the emotional arousal and significantly reduce the malevolent creativity of angry individuals [ 38 ]. Based on previous findings, it is reasonable to expect that cognitive reappraisal and expressive suppression may also attenuate the possible effects of justice sensitivity on anger and then weak the impact on malevolent creativity. Therefore, the current study hypothesizes that emotion regulation can play a moderating role between justice sensitivity and anger.

In conclusion, the aims of the present study were: (1) to reveal the influence of justice sensitivity on malevolent creativity; (2) to investigate whether trait anger as well as state anger played mediating role in the association between justice sensitivity and malevolent creativity; (3) to explore whether emotion regulation moderated the correlation between justice sensitivity and anger. The hypothetical moderated mediation model was shown in Fig.  1 .

The hypothetical moderated mediation model

Participants

Prior to the beginning of the study, we used the G*Power 3.1. provided by Faul et al. to estimate the required sample size [ 47 ]. With setting the medium effect size f 2  = 0.15, α = 0.05, 95% power (1-β err probability), and the number of predictors = 7, the total sample size was 153.

505 college students participated in this study and volunteered for an online survey on the website. A total of 395 valid questionnaires were collected for the study, out of those, 229 (58%) were from male students and 166 (42%) from female students. The major of participants included science and engineering (23%), medicine (25%), literature and history (22%), arts and sports (22%), and others (15%). 347 (88%) were undergraduates, 25 (6%) were postgraduates and 23 (6%) were others.

Justice sensitivity inventory (JSI)

The Justice Sensitivity Inventory developed by Schmitt was used to measure justice sensitivity [ 11 , 48 , 49 ]. Previous studies had shown that the scale had good reliability and was widely used [ 10 , 50 ]. This scale consisted of four subscales: victim sensitivity, observer sensitivity, beneficiary sensitivity, and perpetrator sensitivity. Each subscale consisted of 10 questions and was scored on a 6-point Likert scale (e.g. I cannot easily bear it when others profit unilaterally from me.). This scale was scored from strongly disagree to strongly agree as 1–6. The JSI score is the sum of all the item scores. Higher scores indicated higher justice sensitivity. Cronbach’s α for justice sensitivity in this study was 0.97.

Trait anger scale (TAS)

Trait anger was measured by the Chinese version of the Trait Anger Scale [ 20 , 51 ], which consisted of 10 items (e.g. I’m easily irritated.). Studies showed that the scale had good reliability and validity, and widely used in China [ 52 , 53 ]. This scale was scored on a 4-point Likert scale, and the higher total score indicated higher levels of trait anger. The Cronbach’s α for this scale in this study was 0.87.

State anger scale (SAS)

State Anger Scale was developed by Spielberger and revised into Chinese version by Liu [ 54 , 55 , 56 ]. The scale had been widely used in China [ 57 ]. This scale consisted of 15 items (e.g. I’m angry.) and included three subscales: anger feelings, anger words, and anger actions. This scale was scored on a 4-point scale, with 1 (not at all), 2 (a little), 3 (moderately), and 4 (very strongly). The higher score, the more pronounced state anger. Cronbach’s α for this scale in this study was 0.92.

Emotion regulation questionnaire (ERQ)

Emotion regulation was evaluated by a 10-item self-report version of the Emotion Regulation Questionnaire (ERQ). The scale was developed by Gross and revised into Chinese version by Wang et al. [ 58 , 59 ]. The Chinese version of ERQ had good construct validity, retest reliability, and internal consistency reliability [ 60 , 61 ]. ERQ was consisted of two subscales, including 6 items (e.g. I control my emotions by changing the way I think about the situation I’m in.) for cognitive reappraisal and 4 items for expressive suppression (e.g. I don’t show my emotions.). This scale was rated on a 7-point. The higher total score indicated more frequent use of emotion regulation strategies. Cronbach’s α for this scale in this study was 0.72.

Malevolent creativity behavior scale (MCBS)

Malevolent creativity was measured by MCBS, which developed by Hao et al. and could be used to measure the tendency of individuals to exhibit malevolent creativity behaviors in their daily lives [ 62 ]. The scale had a good ecological validity, covered various forms of malevolent creativity (e.g., deception, tricks, lies), and was easy to administer [ 62 ]. This scale consisted of 13 items and was scored on a 5-point scale, with 1 (not at all)  ∼  5 (always) (e.g., When I am treated unfairly, I will retaliate in a different way). The scores of all items were summed to obtain the total score. The higher total score indicated that the individual showed more malevolent creativity in daily life. Cronbach’s α for this scale in this study was 0.92.

The descriptive statistical analysis and correlation analysis were conducted using SPSS 26.0. Regression analyses were used to test the mediating role of trait anger / state anger between justice sensitivity and malevolent creativity. PROCESS 3.3 was used to test the moderating role of emotion regulation. The demographic variables (gender, major and grade) were entered in the model as covariates.

Common method bias assessment

Harman’s single-factor test was used for exploring the common method bias of the data. All of items of JSI, TAS, SAS, ERQ and MCBS were put into the un-rotated exploratory factor analysis. The results showed that the number of factors with an eigenvalue greater than 1.00 was 17, and the explained variance of the first factor was 28.85, which was lower than the critical criterion of 40% [ 63 ]. The results indicated that there was no obvious common method bias in the data of this study.

Descriptive statistical and correlational analysis

As shown in Table  1 , all of the variables were significantly correlated with each other. The score of JSI was significantly positively correlated with the score of TAS, SAS and MCBS, and were significantly negatively correlated with the score of ERQ. Both TAS and SAS were significantly negatively correlated with ERQ and positively correlated with MCBS.

Analysis of the mediating role of trait anger and state anger

Regression analysis was used to test the mediating role of trait anger and state anger between justice sensitivity and malevolent creativity [ 64 , 65 ].

Trait anger as the mediator

Three regression models were constructed to test the mediating role of trait anger. Firstly, malevolent creativity entered the model as a dependent variable, then demographic variables (gender, major and grade) entered the first block as control variables, and justice sensitivity entered the equation as predictor variable. Secondly, trait anger entered the model as a dependent variable, then demographic variables (gender, major and grade) entered the first block as control variables, and justice sensitivity entered the second block as predictor variable. Finally, malevolent creativity entered the model as a dependent variable, then demographic variables (gender, major and grade) entered the first block as control variables, and justice sensitivity and trait anger entered the equation as predictor variables. The results were shown in Table  2 . It showed that justice sensitivity significantly positively predicted malevolent creativity ( c  = 0.345, t  = 7.262, p  < 0.001) and trait anger ( a  = 0.342, t  = 7.392, p  < 0.001), while trait anger significantly and positively predicted malevolent creativity ( b  = 0.458, t  = 9.839, p  < 0.001). Additionally, the direct effect (path c ’) of justice sensitivity on malevolent creativity was statistically significant ( c ’ = 0.188, p  < 0.001). Therefore, the mediation model was confirmed, which indicated that the relationship between justice sensitivity and malevolent creativity was partially mediated by trait anger. The indirect effect was 0.157 (95% CI [0.111, 0.209]), which accounted for 45.51% of the total effect. The model diagram was shown in Fig.  2 .

The mediating role of trait anger between justice sensitivity and malevolent creativity

State anger as the mediator

Similar to trait anger, hierarchical regression analysis was used to examine the mediating role of state anger. The results were shown in Table  3 . It showed that justice sensitivity significantly and positively predicted malevolent creativity ( c  = 0.345, t  = 7.262, p  < 0.001) and state anger ( a  = 0.277, t  = 5.903, p  < 0.001), while state anger significantly and positively predicted malevolent creativity ( b  = 0.492, t  = 10.958, p  < 0.001). Therefore, the mediation model was confirmed, which indicated that the relationship between justice sensitivity and malevolent creativity was partially mediated by state anger. The indirect effect was 0.136 (95% CI [0.087, 0.188]), which accounted for 39.50% of the total effect. The model diagram was shown in Fig.  3 .

The mediating role of state anger between justice sensitivity and malevolent creativity

Analysis of the moderating role of emotion regulation

Model 7 in PROCESS 3.3 developed by Hayes was used to explore the hypothesized moderated mediation model [ 66 ], as shown in Figs.  4 and 5 , the indirect association between justice sensitivity and malevolent creativity was moderated by emotion regulation. The results showed that the interaction of justice sensitivity and emotion regulation significantly predicted trait anger (B = 0.003, t  = 3.021, p  = 0.003), as well as state anger (B = 0.004, t  = 2.765, p  = 0.006).

The moderated mediation models (trait anger as the mediator)

The moderated mediation models (state anger as the mediator)

As shown in Figs.  6 and 7 , justice sensitivity could significantly and positively predict trait anger ( β  = 0.075, t  = 7.024, p <  0.001) and state anger ( β  = 0.095, t  = 5.532, p <  0.001), when the level of emotional regulation was high. Meanwhile, justice sensitivity could significantly predict trait anger positively ( β  = 0.028, t  = 2.538, p  = 0.012) instead of state anger ( β  = 0.028, t  = 1.560, p  = 0.120), when the level of emotional regulation was low. Additionally, justice sensitivity had a stronger predictive effect on trait anger and state anger when the level of emotion regulation was higher. The results suggested that higher levels of emotion regulation could serve as a buffer against the influences of justice sensitivity on trait anger and state anger among low justice sensitivity individuals. However, the moderating effect of emotion regulation was no longer significant when an individual’s justice sensitivity was high.

The effects of emotion regulation on the mediating pathway of justice sensitivity → trait anger → malevolent creativity (index = 0.0021, SE = 0.0007, 95% CI: [0.0008, 0.0036]) and justice sensitivity \(\to\) state anger \(\to\) malevolent creativity (index = 0.0021, SE = 0.0009, 95% CI: [0.0005, 0.0039]) were all statistical significant. The details were shown in Table  4 . The indirect effects through trait anger were both significant in participants with high and low emotion regulation. Meanwhile, the indirect effects through state anger were significant in participants with high emotion regulation and not those with low emotion regulation.

The interaction effect of JSI and ERQ on TAS

The interaction effect of JSI and ERQ on SAS

To advance the understanding of malevolent creativity, the present study investigated a moderated mediation model to revealed the association between justice sensitivity and malevolent creativity. As hypothesized, the correlation between justice sensitivity and malevolent creativity was mediated by trait anger/state anger. The higher sensitivity to justice, the higher level of trait anger/state anger, which in turn boosted the tendency of malevolent creativity. Additionally, this indirect connection was moderated by emotion regulation. To be specific, the indirect effects through trait anger were both significant in participants with high and low emotion regulation, however, the indirect effects through state anger were significant in participants with high emotion regulation but not those with low emotion regulation.

The association between justice sensitivity and malevolent creativity

The results of this study found that justice sensitivity significantly positively predicted malevolent creativity, which was in line with prior researches. Individuals who were not treated fairly would experience more negative emotions and show more negative behaviors. For example, Brebels et al. found that participants who faced unequal distributional outcomes stole more money from the manager [ 67 ]. Another study found that organizational injustice perception which included procedural justice and interpersonal justice could negatively predict workplace deviance, and the relationships mediated by negative emotion [ 68 ].

Justice is an important means to defend self-benefit in society. The Sensitivity to Mean Intentions Model (SeMI) states that individuals with higher level of justice sensitivity have a lower threshold for perceiving malicious information in offensive or threatening situations compared to individuals with low justice sensitivity. That’s why high justice sensitivity individuals tend to actively search for or focus on information unfavorable to them, and then activate a suspicious mindset after perceiving malicious intentions [ 69 ]. Therefore, individuals with high justice sensitivity were more attentive to unfair stimuli and activated easily by the unfair information, which might prompt them to take steps to defend the fairness and self-benefit [ 10 ]. As a result, these people might tend to engage in more negative deviant behaviors [ 70 ], and be more likely to harm others, i.e., show more malevolent creativity. Previous studies demonstrated that people tend to exhibit malevolent creativity in threatened context, e.g. bullying victimization [ 71 ], unfair [ 13 ]. Clark and James found that perceptions of unfair treatment enhanced instances of negative creativity whereas perceptions of fair treatment yielded more positive creativity [ 13 ]. Another research also found individuals who were more implicitly aggressive and less premeditative were more likely to be malevolently creative in response to situations that provoke malevolent creativity [ 7 ]. These results might indicate that situational perceptions, such as justice and fairness, could influence the degree to which creative products are negative. Therefore, high level justice sensitive may generate high level malevolent creativity.

In other hand, justice sensitive individuals do not entirely behave in accordance with norms of justice, sometimes they could show protest and retaliate more strongly at once when they counter injustice [ 69 ]. For example, researchers found victim-sensitive individuals tended to make unfair offers when they had the power to distribute money at will between themselves and another person [ 72 ]. Another study also showed higher victim sensitivity predicted higher relational, proactive, and reactive aggression, and higher observer sensitivity predicted higher physical and verbal aggression [ 14 ]. Schmitt et al. found that vengeful reactions of laid-off employees toward their former employer depended directly and indirectly—mediated by the perceived fairness of the lay-off procedure—on justice sensitivity [ 73 ]. Meanwhile existing studies found that individuals who tend to break rules or had a weak sense of rule compliance were more likely to possess higher creativity [ 74 ].

In summary, it is plausible that justice sensitivity positively predicts the tendency of malevolent creativity. Individuals with high justice sensitivity are more likely to perceive information about injustice and generate aggressive thoughts and behaviors. This may mean that justice sensitivity individuals also have a tendency to break the rules. The aggressive performance may send a message to the perpetrator that what he has done is reprehensible, at the same time, the performance also is a way to respond injustice in order to avoid similar harm in the future [ 75 ]. Thus, individuals with higher justice sensitivity are more likely to generate aggressive thoughts or behaviors and harm others intentionally, so that resulting in higher level of malevolent creativity.

The mediating role of anger

The results of this study found that both trait anger and state anger mediated the relationship between justice sensitivity and malevolent creativity. Specifically, justice sensitivity could not only directly affect malevolent creativity but also indirectly affect malevolent creativity through trait anger/state anger. This result was consistent with previous studies. Some researchers found that justice sensitivity positively predicted anger [ 38 , 76 , 77 ]. Schmitt et al. described justice sensitivity as: “Individuals differ in how sensitive they are to justice; how easily they are able to perceive injustice; and how strongly they react to perceived injustice” [ 11 ]. Thus, justice sensitivity was a good predictor of an individual’s response to injustice, those with high justice sensitivity tended to respond more strongly to injustice. To be specific, individuals with high justice sensitivity, when confronted with an injustice allocation scheme, would produce a significant increase in the level of negative emotional arousal, which further lead to an increase in anger [ 10 ]. The state of anger, on the other hand, exacerbated the conflict and mistrust in society, undermined the interpersonal interaction and cooperation. Under the emotion of anger, individuals were able to generate more creative and more damaging ideas, which were destructive to society and others [ 38 ]. Lastly, this increased the level of malevolent creativity tendency.

The moderating role of emotion regulation

Our results also revealed that emotion regulation moderated the effect of justice sensitivity on trait anger and state anger. Individuals with high levels of emotion regulation were more likely to avoid anger triggered by justice sensitivity than individuals with low emotion regulation. There is a plausible explanation regarding the moderate role of emotion regulation. As mentioned earlier, individuals who perceive unfairness typically experienced high levels of emotional arousal, while both cognitive reappraisal and expressive suppression emotion regulation strategies were effective in decreasing emotional arousal and implicit aggression [ 38 ]. Thus, individuals with high levels of emotion regulation were better able to regulate anger arising from perceived injustice, which in turn reduced the level of malevolent creativity tendency.

Additionally, the current study found that the moderating effect of emotion regulation was different on trait anger and state anger. Justice sensitivity could positively predict trait anger when the level of emotional regulation was low. One possible explanation for this difference is that the lower level of ERQ might suggest that individuals do not need emotion regulation strategies to manage their emotions frequently. This might indicate that people do not receive external injustice information frequently, so that justice sensitivity as a stable personality trait only can predict the trait anger that has a tighter relationship to it [ 11 ], instead of state anger. Because state anger always depends on the environmental stimuli in the moment.

Although this study revealed possible mechanisms by exploring justice sensitivity influenced on malevolent creativity, there were still some shortcomings. Firstly, justice sensitivity contained multiple components that were not examined separately in this study. Future research could delve into the relationship between different components of justice sensitivity and malevolent creativity. Secondly, this study did not examine whether there was a difference in the role of cognitive reappraisal and expression suppression, which could be further explored in future studies. Future studies also could choose to incorporate other types of emotion regulation strategies and compare the effects of different emotion regulation strategies. Finally, the MCBS was utilized in current study to measure the level of malevolent creativity. Notably, the MCBS, as a measurement tool, could measure potential propensity of malevolent creativity. Some recent studies related to malevolent creativity used malevolent creativity tasks (MCT) to explore malevolent creativity performance. Future research could combine examination of malevolent creativity propensity and malevolent creativity performance to explore the influencing factors and internal mechanisms of malevolent creativity.

In conclusion, the present study validated the association between justice sensitivity and malevolent creativity. The findings illustrated the mediating effect of trait anger/state anger in the pathway from justice sensitivity to malevolent creativity. Additionally, the results also showed evidence of two-way interaction, indicating that emotion regulation moderated the relationship between justice sensitivity and anger. Individuals with high emotion regulation are better able to avoid anger from heightened justice sensitivity than individuals with low emotion regulation.

Data availability

No datasets were generated or analysed during the current study.

Abbreviations

Justice Sensitivity Inventory

Trait Anger Scale

State Anger Scale

Emotion Regulation Questionnaire

Malevolent Creativity Behavior Scale

Standardized deviation

Standard error

95% Bootstrap Confidence interval

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Acknowledgements

The authors would like to thank the college students for agreeing to participate in the study.

This research was supported by the Natural Science Foundation of Shandong Province (ZR2022MC113); the Project of Shandong Province Higher Educational Youth Innovation Science and Technology Program (2019RWF003); the Special Project of Innovation Quality of Educational Sciences Planning of Shandong Province (2022CYB207).

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Yan Wang, Keke Zhang, Fangfang Xu, Yayi Zong, Lujia Chen & Wenfu Li

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Y.W. and K.Z. contributed equally to this work. Y.W. was responsible for the data analysis, methodology, writing of the original draft, review and editing. K.Z. was responsible for the data analysis, and writing of the original draft preparation. F.X., Y. Z. and L. C. were responsible for data analysis and methodology. W.L. was responsible for the conceptualization, reviewing and editing the draft, and funding acquisition. All authors have read and approved the final manuscript.

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Wang, Y., Zhang, K., Xu, F. et al. The effect of justice sensitivity on malevolent creativity: the mediating role of anger and the moderating role of emotion regulation strategies. BMC Psychol 12 , 265 (2024). https://doi.org/10.1186/s40359-024-01759-w

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The moderating role of psychological power distance on the relationship between destructive leadership and emotional exhaustion

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Destructive leadership, a prevalent negative behavior in modern organizations, continues to captivate the interest of scholars and professionals due to its detrimental aftermath. Drawing from social psychological (culture) and conservation of resources theory, we explore the moderating impact of psychological power distance on the link between destructive leadership and emotional exhaustion. The main contribution of this study is that it has created new information about the moderating role of some specific sub-dimensions of psychological power distance (e.g., hierarchy, prestige) in the relationship between destructive leadership and emotional exhaustion. Our findings also reveal a positive correlation between a destructive leadership style and emotional exhaustion. Furthermore, the prestige aspect of psychological power distance amplifies the influence of deficient leadership abilities and unethical conduct on emotional exhaustion. Notably, our study highlights that in the Turkish context, characterized by high power distance, and escalating hierarchies the impact of nepotism disparities on emotional exhaustion. In conclusion, these novel insights underscore a significant research avenue regarding cultural facets.

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Introduction

The actions of leaders in charge of societies and organizations have far-reaching effects, influencing organizational culture and the mental well-being of employees. On the other hand, culture is also recognized to influence various organizational relationships. Concordantly, one cultural element that can explain variations in leadership effectiveness, work attitudes, or job performance is an employee’s power distance orientation (Leonidou et al., 2021 ; Matta et al., 2022 ). Power distance at an individual level also serves as a moderating factor on various aspects, such as the effectiveness of leadership, employees’ perceptions and opinions of their organizations, and the core impacts of HR practices on employees (e.g., Adamovic, 2023 ; Li et al., 2017 ; Loi et al., 2012 ). In this context, despite some recent studies exploring the nature of power and its effects on leader behavior and employee responses from a psychological standpoint (e.g., Kelemen et al., 2020 ; Liao et al., 2021 ; Peng et al., 2021 ), given the extensive influence of psychological power, it is challenging to assert that the literature has fully developed.

According to the Conservation of Resources (COR) theory, resources are broadly defined as objects, personal characteristics, conditions, or energies that are valued because they help one to either directly obtain his or her goals or thwart his or her goal-relevant tendencies (Hobfoll, 2011 ). However, some non-constructive leadership behaviors such as DL continue to threaten the motivation level of employees (Rasid et al., 2013 ), individual resources (Hobfoll, 2011 ), and the welfare of organizations (Brouwers & Paltu, 2020 ; Veldsman, 2012 ). This fact creates a need to understand the contextual development of DL in organizations. DL can be seen as a new type of leadership where leaders engage in systematic and prolonged psychological abuse of subordinates (Ryan et al., 2021 ). Previous research has indicated that the impact of DL may depend on the context, as relationships can vary based on cultural and situational factors (e.g., Burns, 2021 ; Fors Brandebo, 2020 ). In parallel, numerous studies reveal the consequences of such leadership styles (Tepper, 2000 ) and propose theoretical models explaining the mechanisms of these styles (Einarsen et al., 2007 ; Wang et al., 2010 ). More specifically, negative leadership styles have been associated in the current literature with Emotional Exhaustion (EE) (Gkorezis et al., 2015 ; Koç et al., 2022 ), turnover intentions (Badar et al., 2023 ), and counterproductive work behavior (Murad et al., 2021 ). Besides, there is also an increasing research trend regarding the roles played by leadership in the moods and emotions of subordinates (Bono et al., 2007 ; Gooty et al., 2010 ).

“National culture has a crucial role in influencing the occurrence of leadership style” (Zhang & Liao, 2015 , p. 960), and shapes subordinates’ reactions toward these leadership styles (Hofstede, 2001 ; Tepper et al., 2017 ). One of these leadership styles is Destructive Leadership (DL) which is increasing in today’s societal and business areas (Krasikova et al., 2013 ) and prevents proper organizational functioning (Cascio & Aguinis, 2008 ). Organizations invest substantial resources in safeguarding and enhancing employee well-being (Salas-Vallina et al., 2021 ). Within this framework, COR, rooted in the resource-based perspectives of organizations, underscores individuals’ endeavors to safeguard, uphold, and cultivate their resources, highlighting bad management and stress as perceived threats to these resources (Hobfoll, 2011 ). Therefore, based on the COR, enhancing our understanding of the phenomenon of DL by exploring the impact of Psychological Power Distance (PPD) experienced by subordinates on their perceptions of leaders’ abusive behaviors is also significant in terms of decreasing employees’ level of Emotional Exhaustion (EE). EE, is expressed as “feelings of being emotionally drained by one’s work” (Bakker & Costa, 2014 , p. 2), and one of the primary emotional states experienced by employees today. In conclusion, the COR theory as a factor in work-related stress and destructive leadership can be used as a basis to eradicate the harmful effects of destructive leadership for the betterment of professional environments.

As a cultural factor, “Power Distance” (PD) assesses the probability that individuals facing greater inequality within the same social framework will recognize and expect unequal power distribution (Gonzalez, 2021 ). Hence, PD is a distinguishing feature among societies (Meydan et al., 2014 ). In this line, the PD beliefs of subordinates also vary depending on different leadership styles (Yang, 2020 ). For instance, in some cultures, leaders garner respect for taking decisive action, while in others, collaborative and participative decision-making methods hold more significance (Ahmad et al., 2021 , p. 1112). However, the issue of low reliability persists in many power distance scales (Taras, 2014 ). In this context, Adamovic ( 2023 ) contends that the measurement components of the Psychological Power Distance (PPD) scale he created amalgamate a broad power distance aspect and encompass noteworthy, though distinct, facets of power distance. Besides, although the concepts of hierarchy and power are often used as substitute concepts, the distinction between these two concepts has been significantly neglected in previous research (Aïssaoui & Fabian, 2015 ). For example, “in France, employees often do not tolerate power differences, but they tend to value a strong hierarchy” (Adamovic, 2023 , p. 3; d’Iribarne, 1996 ).

However, empirical research on the effects of psychological power distance on DL and related outcomes such as employee EE is surprisingly scarce. This paper seeks to address this gap in the literature. Thus, we aim to explore the potential moderating impact of the newly defined PPD on the connection between DL and EE to achieve trustworthy empirical findings. On the other hand, the study specifically focuses on a sample of Turkish employees, given that Türkiye is the largest economy in the Middle East and Turkish cultural values have had a profound impact on the way organizations are managed in the Middle East region. Besides, most countries in the Middle East were founded with the dissolution of the Ottoman Empire and are societies that come from the same traditions and customs (Lindholm, 2008 ). Thus, as a society that traditionally values respect and compliance with authority, Türkiye represents an ideal context to study the effects of psychological power distance.

Theoretical background

Destructive leadership.

“Destructive leadership is conceptualized as a broad umbrella” (Mackey et al., 2021 , p. 707) that ranges from abusive supervision (Tepper, 2000 ; Tepper et al., 2017 ) to overburdening followers (Schmid et al., 2019 ). Therefore, a wide variety of theories and different approaches, such as the Psychodynamic approach (Pillay & April, 2022 ) or Strain theory (Chen & Cheung, 2020 ) which is a criminological theory, have been used to explain the behaviors and effects of Destructive Leadership (DL). On the other hand, researchers have discovered that a rise in disruptive behaviors, which can deplete individuals’ psychological resources, may stem from factors like heightened anxiety (Byrne et al., 2014 ), work-related stress (Rosenstein, 2017 ), or excessive job pressure (Lam et al., 2017 ). Concurrently, the field of DL is experiencing increased diversity. Within this realm, DL manifests in various structural forms. As per Einarsen et al. ( 2007 ) and Larsson et al. ( 2012 ), these forms can be categorized as active or passive. Active behaviors encompass traits such as arrogance, unfairness, and intimidating or disciplining subordinates. Passive behavioral patterns highlight leader qualities like disinterest, avoidance of conflict, or poor planning skills (Larsson et al., 2012 ). Active behaviors are systematic and deliberate, while passive forms indicate deficiencies in leaders’ work and responsibilities (Einarsen et al., 2007 ). In addition, DL is divided into two dimensions in the literature: task and relationship. Task-related behaviors represent perceptions of the leader’s competence, including:

Isolation from outside interference and excessive control.

Lack of determination and uncertainty.

Stress and loss of control.

The relationship-related behaviors dimension refers to the leader’s skills in human relations, such as:

Low ability to relate to colleagues and subordinates and lack of job satisfaction.

Lack of understanding and self-centered behavior (Fors et al., 2016 ).

On the other hand, “employees will attribute leadership behavior in the process of interaction with the leaders” (Jiao & Wang, 2023 , p. 2), and the psychological states of subordinates will also be affected depending on their attribution. As a result, based on attribution theory (Heider, 1958 ; Weiner, 1985 ), it should not be ignored that whether the above-mentioned behaviors will be perceived as destructive or non-destructive may differ depending on the psychological state and perceptions of the subordinates in addition to cultural impact (Kong & Jogaratnam, 2007 ; Ojo, 2012 ).

Emotional exhaustion

Burnout, a psychological syndrome brought on by a prolonged reaction to ongoing workplace stressors (Maslach et al., 2001 ), is a significant issue that is becoming worse as workers are subjected to increasing pressure and demands from their managers under different cultural contexts (Rattrie et al., 2020 ). Moreover, burnout has been associated with several negative organizational outcomes, including job performance, emotional labor, and reduced employee well-being (Moon & Hur, 2011 ; Qiu et al., 2023 ; Maslach et al., 2001 ). “It is generally accepted to encompass three dimensions that occur in a developmental sequence” (Strack et al., 2015 , p. 578): from emotional exhaustion (EE) to depersonalization and subsequent decline in achievement (Cordes & Dougherty, 1993 ; Maslach et al., 2001 ). Within this framework, EE refers to the extent to which an individual is depleted or lacking in physical and psychological resources to cope with an interpersonal stress situation (Maslach et al., 2001 ). Employees who experience EE at work feel extremely stressed because they lose their physical and mental endurance (Obi et al., 2020 ) which ultimately leads to unhealthy tendencies as well as anxiety, stress, and depression (Bianchi et al., 2015 ; Weigl et al., 2017 ). On the other hand, the main characteristics of EE at the organizational level are the desire to quit work, absenteeism, and low morale (Maslach, 1996 ). Ultimately, the chronic experience of negative emotions in both individual and organizational contexts and the difficulties employees experience in regulating them can deplete their cognitive and emotional resources, which emerges as an important risk factor for EE (Chang, 2009 ; Hsieh et al., 2011 ).

Destructive leadership and emotional exhaustion

Different leadership styles have a known impact on employees’ emotions (Baig et al., 2021 ), and “employees’ perceptions about the leader are likely to affect their attitudes” (Gkorezis et al., 2015 , p. 622). In this context, destructive leadership styles (e.g., abusive supervision, petty tyranny, negative leadership) may trigger negative emotional reactions from employees (Schilling & Schyns, 2015 ), and increase employees’ emotional exhaustion (Chi & Liang, 2013 ). According to the Emotional Dissonance theory, negative supervision may also lead employees to conceal their true emotions (Naseer & Raja, 2021 ). Thus, scholars are focusing on leaders’ negative behavioral impact on employees’ emotional exhaustion levels (Gkorezis et al., 2015 ) to increase employee well-being at work (Hetrick et al., 2022 ). In addition, interpersonal stressors that diminish the well-being of employees are frequently experienced within the organizational atmosphere dominated by DL due to the nature of this harmful style (Hetrick et al., 2022 ). According to the Emotional Dissonance theory, negative supervision may also lead employees to conceal their true emotions (Naseer & Raja, 2021 ). Within this framework, scholars are focusing on leaders’ negative behavioral impact on employees’ emotional exhaustion levels (Gkorezis et al., 2015 ) to increase employee well-being at work (Hetrick et al., 2022 ), interpersonal stressors that diminish the well-being of employees are frequently experienced within the organizational atmosphere dominated by DL due to the nature of this harmful style (Hetrick et al., 2022 ), this article bases the theoretical connection between Destructive Leadership (DL) and Emotional Exhaustion (EM) on the definition of Einarsen et al. ( 2007 ).

[..] is the systematic and repeated behavior of a leader or manager that harms the organization’s legitimate interests by undermining the organization’s resources, and effectiveness, motivation, and job satisfaction of subordinates (p. 208).

Current studies have explored the positive relationship between despotic, toxic, and destructive leadership with emotional exhaustion (e.g., Shahzad et al., 2023 ; Koç et al., 2022 ). In this context, “meta-analytic evidence demonstrates that DL has negative consequences for followers’ workplace behaviors (e.g., job performance, organizational citizenship behaviors [OCBs], workplace deviance)” (Mackey et al., 2019 , p. 3). These empirical findings may suggest that DL outputs could also result in EM among employees. In conclusion, destructive leaders can cause fundamental problems in business life, such as increasing the level of emotional exhaustion (Krumov et al., 2016 ), and subordinates who are constantly exposed to leaders’ destructive practices experience frustration and emotional exhaustion (Glasø & Vie, 2009 ). Given the theoretical discussions above, the research’s first hypothesis was formulated as follows.

H1: There is a positive relationship between destructive leadership and emotional exhaustion.

Psychological power distance

The PPD concept originates from a multidisciplinary field of study called cross-cultural psychology, which seeks to understand how culture impacts the cognitive and behavioral outcomes of individuals and groups (Yang, 2020 ). According to Hofstede ( 1991 , p. 27), “power distance can be described as the degree to which individuals who are less powerful within a country’s institutions and organizations anticipate and acknowledge the unequal distribution of power”. In this context, “Shore and Cross ( 2005 , p. 57) underlined that power is distributed more equitably in low power distance cultures and unequally in high power distance cultures. For instance, the power distance index (Khakhar & Rammal, 2013 ) shows that the Arab world, which values traditional authority highly (Inglehart, 1997 ), scores highly in this index, and people working in these cultures strictly follow higher hierarchical orders (Chiaburu et al., 2015 ; Korkmazyurek & Korkmazyurek, 2023 ). In summary, “individuals who score highly on psychological power distance also tend 1) accept and tolerate power differences in the workplace, 2) avoid conflict with authority figures, 3) prefer a clear hierarchy at work, 4) strive for status and prestige, and 5) expect a social distance between managers and employees.” (Adamovic, 2023 , p. 2).

Psychological power distance as a moderator

It has been suggested that various cultures have their norms regarding what constitutes good or bad leadership, and these norms may be reflected in the perception of psychological power distance (Tang et al., 2020 ). In this context, numerous studies explore the connection between PD and leaders’ influence tactics as PD decides if subordinates in a culture would accept a leader’s influence and the specific situations in which a leader might face resistance from a group of subordinates. Thus, investigating the role of PD in employees’ perception of leaders and better understanding the impact of leaders on employee well-being, will not only inform practices for workplace health intervention but also enlighten leadership researchers in discussing the universal and contingency theory of leadership. On the other hand, several power distance measures, like the ones created by Cable & Edwards ( 2004 ), Dorfman & Howell ( 1988 ), and Maznevski and colleagues ( 2002 ), have produced interesting findings on the importance of power distance concerning employee results and leadership (Adamovic, 2023 , p. 2). As an example, Tepper ( 2007 ) claims that “countries with high power distance experience more abusive supervision”. Thus, individuals characterized by large power distance have a high tolerance for lack of autonomy and rely more on centralization and formalization of authority (Hofstede, 1980 ). In this context, PPD influences how people feel, think, and act about problems of status and power at work and is crucial in understanding how leaders and subordinates interact (Adamovic, 2023 , p. 1).

The moderating impact of PD on the link between workers’ job satisfaction, performance, and absenteeism was highlighted by Lam and Friends ( 2002 . p.14). On the other hand, Farh and Friends ( 2007 : 721) found in their study that PD had a negative moderating effect on the relationship between work outcomes such as organizational commitment, job performance, and conscientiousness. According to the findings above, we can argue that PPD has a deterministic effect on the functioning of the theoretical mechanisms between DL and EM. Besides, In countries with high power distance, abuse by superiors is quite normative and consistent for subordinates in superior-subordinate relationships (Tepper, 2007 ). In this regard, the need for power in the prestige dimension (Carl et al., 2004 ; Hofstede, 2001 ; Schwartz, 2014 ), which is an organic extension of previous power distance studies, is also associated with narcissistic and Machiavellian actions and attitudes (Jonason et al., 2022 ). “People with high power distance orientation in the workplace typically accept status disparities, whereas people with low power distance orientation frequently support treating everyone equally regardless of status symbols.” (Adamovic, 2023 , p. 3). Conversely, workers with a low power distance orientation favor participatory leadership and decision-making processes which is not as prevalent under abusive supervision (Rao & Pearce, 2016 ). As a result, in countries with low power distance, abusive supervision may affect the emotional state of subordinates (Meydan et al., 2014 ).

De Clercq and colleagues ( 2021 ) discovered that PPD is positively associated with subordinates’ perception of superiors’ destructive leadership. This indicates that when subordinates perceive high PPD, they are more likely to view their leaders as engaging in such destructive behaviors. This correlation can also be attributed to abusive supervision, a form of destructive leadership behavior. According to social learning theory (Rumjaun & Narod, 2020 ), when leaders’ power is internalized and reflected as subordinates’ psychological power distance, the aggressive behaviors displayed by leaders are likely to be observed and learned by subordinates. These aggressive behaviors could then lead to emotionally exhausting reactions in the subordinates. Correlationally, this study suggests that psychological power distance mediates the link between destructive leadership behaviors and subordinates’ emotional exhaustion levels.

H2: Psychological power distance has a moderating role in the relationship between perceived destructive leadership and emotional exhaustion.

The causal research method which is one of the quantitative research methods is used in the study. Cross-sectional data were collected using an electronic survey form through the convenience sampling method. Participants received the link to the electronic survey form via social media and email. The statistical analyses were carried out with AMOS 24 and SPSS 27.

Sample and procedure

The survey sample size was determined by the Non-random convenience sampling method and the process of its determination was as follows: The sample size that can numerically represent the universe of working people was calculated with the formula below (Ding et al., 2022 ).

In this formula, n represents the required sample size and Z represents the z-statistic at a 90% confidence level (Z = 1.64). σ represents the standard deviation of the overall population and takes the value of 0.5. d is the tolerance error or sampling error. It is the difference between the universe parameter and the statistical value obtained from the sample. Since such a research model has not been studied before in the Turkish culture, the tolerance error for the sample was accepted as 10%. The final required sample size was calculated as 67.

Along this line, in a homogenous group with a reliability of 0.90 and a sampling error of 0.10, a sample group of 61 people can represent a universe of 100 million people (Yazıcıoğlu & Erdoğan, 2004 ). Moreover, in a heterogeneous group, a sample size of 96 is sufficient. Data were collected from a total of 222 employees working in different jobs by using the convenience sampling method via an online survey. This sample strategy allows us to collect data that covers more industries in Türkiye. The participation of participants in the research was voluntary. Working in a workplace was the only criterion for participants. In this context, it was accepted that the sample size was large enough to represent the universe.

117 (53%) of the participants were female, and 105 (47%) were male. The participants have 14.48 (sd = 10.46) mean years of working experience, while their age was between 19 and 67 years, with a mean value of 40.87 (sd = 9.42). % 24.9 of the participants were between 19 and 34, % 24.9 between 35 and 40, % 25.8 between 41 and 47, and % 24,4 between 47 and 67 years old.

Measure of psychological power distance

The PPD perception was measured with the scale, developed by Adamovic ( 2023 ). This scale is a five-point Likert-type scale (1 strongly disagree to 5 strongly agree) comprising fifteen items under five factors (Power, Conflict with Authority Figure, Hierarchy, Prestige, and Social Distance). Ascending numbers indicate the extent to which power distance was perceived. The overall original psychological power distance scale demonstrated strong reliability (α = 0.82). The scale has not been used in Turkish before. For this reason, firstly, this scale was adapted to Turkish, and then the validity and reliability of the scale were tested. By adopting this scale, the method suggested by Brislin et al. ( 1973 ) was used. This method includes five basic steps: translation into the target language, evaluation of the translation into the target language, back-translation into the source language, evaluation of the back-translation into the source language, and final evaluation with experts. After the adaptation process, exploratory factor analysis was applied for the validity of the scale. In the exploratory factor analysis, the Principal Axis Analysis method and the Varimax Rotation Technique were applied to calculate factor loadings. Factors with eigenvalues greater than 1 were taken into consideration. As a result of the factor analysis, it was seen that all factor loadings were higher than 0.30 and there were no overlapping items. The lowest factor loading value recommended for a good factor analysis is 0.30 (Tavakol & Wetzel, 2020 ). As a result of the exploratory factor analysis, the KMO (Kaiser-Meyer-Olkin) value was found to be 0.76, and the result of Bartlett’s test was found to be p  < 0.001. After that, Confirmatory Factor Analysis was performed to examine the structural validity of the measurement tool. The single-factor, first-level related, unrelated, and second-level related models were tested and Psychological Power Distance Scale showed the highest goodness of fit in the first-level related model (Δχ2 = 145.18, p  < 0.001, SD = 79, Δχ2/SD = 1.84, RMSEA = 0.06, CFI = 0.92, IFI = 0.92, TLI = 0,90) which verified it’s original five-factor dimension. In our study, the Psychological Power Distance scale showed generally strong reliability (α = 0.78).

Measurement of destructive leadership

Destructive Leadership was measured with the scale which was developed by Aydinay ( 2022 ). Five Point Likert-type scale (1 strongly disagree to 5 strongly agree) comprises 26 items under five factors (Inadequate leadership skills and unethical behaviors, Authoritarian leadership, Inability to deal with new technology and other changes, Nepotism (favoritism), Callousness toward subordinates). Ascending numbers indicate the extent to which destructive leadership was perceived. The original scale’s reliability was reported using Cronbach’s coefficient alpha of α = 0.97, which showed that the scale was reliable. The validity of the scale was tested with confirmatory factor analysis, (Δχ2 = 590.23, p  < 0.01, SD = 280, Δχ2/SD = 2.11, RMSEA = 0.07, CFI = 0.94, IFI = 0.94, TLI = 0,93) which verified it’s original five-factor dimension. In our study, the Destructive Leadership scale showed generally strong reliability (α = 0.97).

Measurement of emotional exhaustion

In the study, to measure the emotional exhaustion levels the emotional exhaustion dimension scale in the Maslach Burnout Inventory (MTE) (Maslach et al. 1996 ), translated into Turkish by Ergin ( 1992 ), was used. Five-point Likert-type scale (1 strongly disagree to 5 strongly agree) comprises 9 items under one factor. The original scale’s reliability was reported using Cronbach’s coefficient alpha of α = 0.86, which showed that the scale was reliable. The validity of the scale was tested with confirmatory factor analysis, (Δχ2 = 40.51, p  < 0.006, SD = 21, Δχ2/SD = 1.93, RMSEA = 0.07, GFI = 0.96, CFI = 0.98, IFI = 0.98, TLI = 0,97) which verified it’s original one-factor dimension. In our study, the Emotional Exhaustion scale showed generally strong reliability (α = 0.91).

Table  1 displays the variables’ descriptive statistics as well as the Pearson correlation coefficients. Examining the correlations between the variables of the study, all sub-dimensions of Destructive Leadership [Inadequate leadership skills and unethical behaviors ( r  = 0.46, p  < 0.01), Authoritarian leadership ( r  = 0.49, p  < 0.01), Inability to deal with new technology and other changes ( r  = 0.42, p  < 0.01), Nepotism ( r  = 0.40, p  < 0.01), Callousness toward subordinates ( r  = 0.40, p  < 0.01)] were positively correlated with emotional exhaustion. There was no correlation between Psychological Power Distance sub-dimensions (Power, Conflict with Authority Figures, Hierarchy, Prestige, and Social Distance) and emotional exhaustion. Moreover, there was no correlation found between sub-dimensions of Destructive Leadership and Psychological Power Distance.

Using SPSS 27 software, a multiple regression analysis was executed to test the research’s first hypothesis. First of all, by controlling the effects of gender and age, the direct relationship between destructive leadership dimensions and emotional exhaustion was examined. Table  2 displays the analysis findings. The variance explained by emotional exhaustion in this model is R2 = 0.31. The results indicate that Authoritarian leadership (b = 0.29, p  < 0.01) is positively and significantly associated with emotional exhaustion. The study’s first hypothesis is supported by this result. The other sub-dimensions of Destructive leadership (Inadequate leadership skills and unethical behaviors, Inability to deal with new technology and other changes, Nepotism, and Callousness toward subordinates) aren’t associated with emotional exhaustion ( p  > 0.05).

Next, to test hypothesis 2, the moderating effect of psychological power distance sub-dimensions in the relationship between destructive leadership sub-dimensions and emotional exhaustion was examined through SPSS PROCESS 4.1 macro (Hayes, 2018 ). Totally twenty-five regression analyses were conducted. Model 1 of the PROCESS was applied in all analyses, based on 5000 bootstrap samples. As a result of all analyses, it was found that the Inadequate Leadership Skills and Unethical Behaviors X Prestige interaction variable (b = 0.17, 0.05 < 95% CI < 0.28), the Inability to Deal with New Technology and Other Changes X Hierarchy interaction variable (b = 0,13, 0.02 < 95% CI < 0.24), the Inability to Deal with New Technology and Other Changes X Prestige interaction variable (b = 0.16, 0.05 < 95% CI < 0.27), Nepotism X Hierarchy interaction variable (b = 0.10, 0.009 < 95% CI < 0.20) was significantly and positively associated with emotional exhaustion. All the other interactions were not significant.

To understand the interaction of Inadequate Leadership Skills and Unethical Behaviors X Prestige interaction, we examined the levels of independent variables based on the levels of moderator variables. To establish low and high values as a default setting, the 16th and 84th percentiles of the moderator variable by PROCESS are taken into consideration (Hayes, 2018 ). In our analysis when the prestige level is low, the association between Inadequate Leadership Skills, Unethical Behaviors, and emotional exhaustion (b = 0.25, 0.11 < 95% CI < 0.39), was relatively low. In contrast, when the prestige was high, Inadequate Leadership Skills and Unethical Behaviors were relatively highly related to emotional exhaustion (b = 0.54, 0.40 < 95% CI < 0.67). As the level of prestige increases, Inadequate Leadership Skills, Unethical Behaviors, and emotional exhaustion association also increase. This demonstrates that prestige strengthens the relationship between Inadequate Leadership Skills and Unethical Behaviors and Emotional Exhaustion. Along this line, we can say that prestige positively moderates the relationship between Inadequate Leadership Skills and Unethical Behaviors, and Emotional Exhaustion. This result validates the study’s second hypothesis.

To figure out the mechanism of the moderating effect of prestige, a simple slope plot was drawn as seen in Fig.  1 . It shows that in the case of a low level of prestige (dashed line) the increase in the Inadequate Leadership Skills and Unethical Behaviors leads to a moderately significant difference in emotional exhaustion. However, in the case of a high level of prestige (dashed straight line) differences in the Inadequate Leadership Skills and Unethical Behaviors lead to a relatively higher significant change in emotional exhaustion.

Simple slope for inadequate leadership skills and unethical behaviors by Prestige interaction

To understand the interaction of the Inability to Deal with New Technology and Other Changes X Hierarchy interaction, we examined the levels of independent variables according to the levels of moderator variables. To establish low and high values as a default setting, the 16th and 84th percentiles of the moderator variable by PROCESS are taken into consideration (Hayes, 2018 ). In our analysis when the hierarchy level is low, the association between Inability to Deal with New Technology and Other Changes and emotional exhaustion (b = 0.28, 0.12 < 95% CI < 0.43), was relatively low. On the contrary, when the hierarchy was high, the Inability to Deal with New Technology and Other Changes was relatively highly related to emotional exhaustion (b = 0.49, 0.35 < 95% CI < 0.63). As the level of hierarchy increases, the Inability to Deal with New Technology and Other Changes and emotional exhaustion association also increase. This shows that hierarchy strengthens the relationship between the Inability to Deal with New Technology and Other Changes and emotional exhaustion. Accordingly, we can say that hierarchy positively moderates the relationship between the Inability to Deal with New Technology and Other Changes and emotional exhaustion. This finding supports the second hypothesis of the study.

To figure out the mechanism of the moderating effect of hierarchy, the basic slope plot was created, as seen in Fig.  2 . It shows that in the case of a low level of hierarchy (dashed line) the increase in the Inability to Deal with New Technology and Other Changes leads to a moderately significant difference in emotional exhaustion. However, in the case of a high level of hierarchy (dashed straight line) differences in the Inability to Deal with New Technology and Other Changes lead to a relatively higher significant change in emotional exhaustion.

Simple slope for inability to deal with technology and other changes by Hierarchy interaction

To understand the interaction of the Inability to Deal with New Technology and Other Changes X Prestige interaction we examined the levels of independent variables according to the levels of moderator variables. To establish low and high values as a default setting, the 16th and 84th percentiles of the moderator variable by PROCESS are taken into consideration (Hayes, 2018 ). In our analysis when the prestige level is low, the association between the Inability to Deal with New Technology and Other Changes and emotional exhaustion (b = 0.27, 0.13 < 95% CI < 0.42), was relatively low. On the contrary, when the prestige was high, the Inability to Deal with New Technology and Other Changes was relatively highly related to emotional exhaustion (b = 0.54, 0.39 < 95% CI < 0.68). As the level of prestige increases, the Inability to Deal with New Technology and Other Changes, and emotional exhaustion association also increase. This shows that prestige strengthens the relationship between the Inability to Deal with New Technology and Other Changes and emotional exhaustion. Accordingly, we can say that prestige positively moderates the relationship between the Inability to Deal with New Technology and Other Changes and emotional exhaustion. This finding supports the second hypothesis of the study.

To figure out the mechanism of the moderating effect of prestige, the basic slope plot was created, as seen in Fig.  3 . It shows that in the case of a low level of prestige (dashed line) the increase in Inability to Deal with New Technology and Other Changes leads to a moderately significant difference in emotional exhaustion. However, in the case of a high level of prestige (dashed straight line) differences in Inability to Deal with New Technology and Other Changes lead to a relatively higher significant change in emotional exhaustion.

Simple slope for inability to deal with technology and other changes by Prestige interaction

To figure out the mechanism of the moderating effect of the Nepotism X Hierarchy interaction we examined the levels of independent variables according to the levels of moderator variables. To establish low and high values as a default setting, the 16th and 84th percentiles of the moderator variable by PROCESS are taken into consideration (Hayes, 2018 ). In our analysis when the hierarchy level is low, the association between Nepotism and emotional exhaustion (b = 0.21, 0.08 < 95% CI < 0.33), was relatively low. On the contrary, when the hierarchy was high, Nepotism was relatively highly related to emotional exhaustion (b = 0.38, 0.26 < 95% CI < 0.49). As the level of hierarchy increases, Nepotism, and emotional exhaustion association also increase. This shows that hierarchy strengthens the relationship between Nepotism and emotional exhaustion. Accordingly, we can say that hierarchy positively moderates the relationship between Nepotism and emotional exhaustion. This finding supports the second hypothesis of the study.

To figure out the mechanism of the moderating effect of hierarchy, the basic slope plot was drawn as seen in Fig.  4 . It shows that in the case of a low level of hierarchy (dashed line) the increase in Nepotism leads to a moderately significant difference in emotional exhaustion. However, in the case of a high level of hierarchy (dashed straight line) differences in Nepotism lead to a relatively higher significant change in emotional exhaustion.

Simple slope for nepotism by Hierarchy interaction

Discussion and conclusion

The study holds some important theoretical implications. The findings of this study offer valuable insights into the moderating role of newly conceptualized Psychological Power Distance (PPD) on the relationship between Destructive Leadership (DL) and Emotional Exhaustion (EE). First, our empirical results support previous research findings regarding the positive correlation between negative leadership styles such as toxic and narcissistic (e.g., Badar et al., 2023 ) and EE. Similar to the findings of Abubakar et al.‘s ( 2017 ) study on nepotism and workplace withdrawal, nepotism, one of the components of DL, was found to be positively correlated ( r  = 0.40, p  <.01) with EE. Hierarchy and nepotism pose risks in different forms (Tytko et al., 2020 ). A great example is the risk of possible downfall when concentrating all power on a small group of privileged relatives and the organs of that group. Also, it explains in terms of the risk of mutual support under hierarchy. When a society is structured in a certain way and has a social structure that allows powerful groups to protect each other’s interests, as is common in Middle Eastern societies, the privilege of that group is guaranteed (Shamaileh & Chaábane, 2022 ). And this mutual reinforcement can be a stable thing over time. Subsequently, the risks and consequences of these dysfunctional and corruptive practices in the context of organizational performance may also be possible.”

On the other hand, we can discuss our empirical results based on the concept of Perceived external prestige (Kamasak & Bulutlar, 2008 ) and social identity theory (Tajfel & Turner, 2004 ), which are also shaped around the concepts of social value and status. “According to the literature, perceived external prestige, unlike corporate image or corporate reputation, is based on employees’ beliefs (Šulentić et al., 2017 ). Within this framework, when we look at the items measuring the prestige dimension of PPD, we encounter statements that gaining respect and status is important for employees to exert social influence (Cheng et al., 2013 ). Previous studies have shown that the concept of prestige/status is also associated with EE (Sessions et al., 2022 ) and narcissism which is one of the main characteristics of DL (e.g., Cheng et al., 2010 ; Haertel et al., 2023 ; Zeigler et al., 2019 ). Correlationally, our empirical findings also show that prestige also strengthens the relationship between inadequate leadership skills, unethical behaviors, and EE. Besides, This study also created new information about the moderating role of some specific sub-dimensions of PPD in the relationship between DL and EE. Furthermore, the prestige aspect of PPD amplifies the influence of deficient leadership abilities and unethical conduct on EE.

On the other hand, “hierarchies may produce undesirable or dysfunctional consequences” in organizations (Magee & Galinsky, 2008 ; Leavitt, 2005 ). In this context, the fact that differences in Nepotism lead to a relatively higher significant change in EE in case the hierarchy is high can be considered as an empirical finding that may lead to undesirable and dysfunctional results in the organizational context. Additionally, in the context of this research, a specific mediating link of prestige/status seeking emerged in the effect of destructive leadership style on EE. Although this finding is limited, it can be generalized to Middle Eastern countries (e.g., Syria, Lebanon, Iraq) where power distance is high. Therefore, The study holds some managerial implications. These results may assist organizations and leadership training experts in developing interventions to reduce employee EE and abusive supervisory behaviors. In addition, the interactional effect of high levels of hierarchy on the relationship between DL and EE also sheds light on the cultural and psychological processes in societies where PPD is high.

When the prestige was high, the Inability to Deal with New Technology and Other Changes was relatively highly related to EE (b = 0.54, 0.39 < 95% CI < 0.68). In other words, as the level of prestige increases, the challenges of inability to adopt new technology, and EE association also increase. This shows that prestige strengthens the relationship between the challenges of the inability to adopt new technology and EE. Most of the time, it is seen that introducing new technology to the workplace may create fear in the employees. Techniques such as training, communication, and support should be used by the management to minimize these challenges (Ivanov et al., 2020 ). In this context, power distance measures the extent to which subordinates accept control from their leaders or supervisors, or the extent of freedom that they can practice their own beliefs, values, and behaviors (Guzman & Fu, 2022 ). In high power distance cultures, subordinates are not allowed to raise their voice to their supervisor- no matter right or wrong they think. Traditionalists think that this rigid stratification may stop the implementation of new technology developed in high power distance countries like China or Malaysia (e.g., Rithmire, 2023 ). In conclusion, our empirical findings point out a well-established path for future study which focuses on the impact of power distance theories on organizational behavior like technology adaptation.

In a recent research by Harms and his colleagues, they found that the negative effects of DL on EE (Hattab et al. 2022 ) could be reduced by PPD. This means that when subordinates do not perceive a large power distance between them and their leaders, the harmful effect of DL on EE may be minimized. This may be because when subordinates have low PPD from their leaders, they are likely to question the necessity of coping with EE and evaluate demands made by the leaders. On the contrary, when subordinates perceive a large psychological power distance, they are more likely to accept the situation and engage in EE because it is a reflection of decorum or cultural practices. Moreover, it has been suggested that different cultures have their norms about what is good or bad leadership, and these norms can be reflected in the perception of PPD. Investigating the role of power distance in employees’ perception of leaders and better understanding the impact of leaders on employee well-being, will not only inform practices for workplace health intervention but also enlighten leadership researchers in discussing the universal and contingency theory of leadership.

The study of the moderating role of PPD will be advantageous for the following reasons. Firstly, fostering a transparent and merit-based work culture in high PPD countries is a way to reduce and prevent nepotism in the employment setting (Kirya, 2020 ). Secondly by identifying how and under what circumstances different types of leaders may impact differently on employees, human resource practitioners will have better insight into leader selection and training. For example, if an organization operates in a relatively low power distance culture such as the United States (Hofstede, 2001 ), then the findings from our research suggest that both supportive and directive leadership styles may be effective in reducing employees’ EE. However, if the same organization aims to expand to countries with higher power distance, especially those in the Middle East, it will be beneficial to have leaders with less directive and more supportive leadership styles in charge. In this circumstance, human resource practitioners can use the cultural dimensions of different societies such as power distance to evaluate the suitability of leaders’ approaches and make necessary modifications.

Limitations and future research directions

Because most concepts in the social sciences are interrelated in some way, theoretical frameworks that provide a holistic perspective are particularly important in facilitating research.

Therefore, in order not to expand the scope of the research too much, thereby the theoretical structure is limited to specific correlated variables. On the other hand, organizations are rich in complex interpersonal interactions, and these relational dynamics, combined with unique organizational factors, can differentiate the relationship of DL and EE from one organization to another. Besides, culture is another important antecedent predictor in terms of organizational outcomes. For example, the culture that affects the follower’s questioning, ethical decision-making, or tolerance of the unethical behavior of the supervisor (Cohen, 1995 ). Therefore, it should not be ignored that the level of EE may differ depending on the level of exposure to DL along with culture, situational circumstances, level of motivation, or personality characteristics of employees.

The PPD is a newly conceptualized notion. In this context, we also believe that the hypothesis and the results of our study may lead to new research questions for unexplored fields. In addition, there are several practical implications within our research. We explained the organic mechanism between DL and EE under the moderation of PPD. In this sense, employees and practitioners need to understand the motivation and psychological contract (Rousseau, 1990 ) level of employees under the reign of DL. On the other hand, based on our conceptual elaboration in this paper, we also suggest future research on gender, because, male and female forms of destructive leadership can differ significantly in terms of gender barriers and catalysts such as role fit. Finally the complex nature of the research variables, qualitative research is needed to explore which individual resources (e.g., self-efficacy) or behaviors will be negatively affected to cope with destructive leaders. As an additional comment, the significant effect of hierarchy on emotional exhaustion in the Turkish sample, where power distance is high (Hofstede, 2001 ) could be a starting point for future research in the context of the impact of cultural homogenization/globalization via internal uniformity (Conversi, 2014 ).

This study has some limitations. First of all, this study is conducted in a rather small region in Türkiye. Examining this research model at the international level in different provinces and different cultures will make strong contributions to the literature. The second is the investigation of the proposed causal relationship through the application of a cross-sectional research methodology. Thirdly, because our study relies on self-reported questionnaires, it is susceptible to common method biases and social desirability.

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Korkmazyurek, Y., Ocak, M. The moderating role of psychological power distance on the relationship between destructive leadership and emotional exhaustion. Curr Psychol (2024). https://doi.org/10.1007/s12144-024-06016-2

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