Organizational Justice
Social Exchange Theory in Organizational Justice
(Blau, 1964). This theory suggests that supportive behaviors by an authority can be viewed as a benefit to an employee that should trigger an obligation to reciprocate. That obligation to reciprocate can then be expressed through positive discretionary behaviors As applied in the justice literature, this core theoretical premise can be used to explain findings such as the positive relationship between justice perceptions and citizenship behavior (Masterson, Lewis, Goldman, & Taylor, 2000; Organ, 1990).
Greenberg (1987) Taxonomy
2x2 taxonomy. One of the taxonomy dimensions was process vs. outcome, reflecting the distinction between procedural and distributive justice. The other dimension was reactive vs. proactive, with the former focusing on reactions to just and unjust events and the latter focused on the behaviors that can create just events Notes that Adam's Equity theory is an example of the outcome-reactive quadrant Leventhal's (1976) allocation norms was deemed outcome-proactive
Categorization Approach to Justice
Ambrose and Kulik's (2001) Categories are cognitive structures that represent the features of a given stimulus. Category prototypes are special structures that include all of the essential features of a given category. Ambrose and Kulik (2001) suggest that the justice rules identified by Thibaut and Walker (1975), Leventhal (1976, 1980), Bies and Moag (1986), and others represent the features of a category prototype for justice, though some rules may be more essential to the prototype with others being more peripheral. From this perspective, a given decision event is judged to be fair when the event's features match the central elements of the justice category prototype.
Meta-Analysis on Mood, Trait Affectivity and Justice
Barsky and Kaplan (2007) meta analysis on the effects of both mood and trait affectivity on measures of procedural, distributive, and interactional justice. The results yielded moderately strong correlations between mood and the justice dimensions. Positive mood was associated with more favorable justice perceptions and negative mood was associated with less favorable justice perceptions. The magnitude of the positive and negative mood effects were quite similar. The results yielded somewhat weaker, but still significant correlations between trait affectivity and the justice dimensions. As with the mood results, the magnitude of the positive and negative affectivity effects were similar. It therefore appears that positive affect and negative affect can "move the needle" on justice perceptions to a similar degree. Findings may be due to common method bias (Podsakoff, MacKenzie, Lee, & Podsakoff, 2003) or due to people actually being treated unfairly
Justice and Emotion
Bies and Tripp (2002) note that employees feel injustice on an emotional basis—reporting anger, bitterness, and fear in connection with violations of justice rules. The authors suggested that the justice literature has focused more on the cognitive "high ground" than the emotional "valley of darkness" associated with experiences of injustice. Similarly, Folger, Cropanzano, and Goldman's (2005) discussion of fairness theory and the deontic model notes that the capacity to reason about justice operates simultaneously with a sense of anger that results from violations of moral standards.
Distinguishing Justice Types by Foci
Blader and Tyler (2003) organization vs. manager-originating distinction as "formal justice" vs. "informal justice" Rupp and Cropanzano (2002) utilized the terms "organizational justice" vs. "supervisory justice."
Interpersonal and Informational Justice
Breaking up interactional justice Greenberg (1993b) argued that the respect and propriety rules (interpersonal justice) are distinct from the justification and truthfulness rules (informational justice) labeling the former criteria interpersonal justice and the latter criteria informational justice.
Overall Fairness as Moderator
Choi (2008) cast overall fairness as a moderator of the relationship between specific justice dimensions and attitudinal and behavioral outcomes, consistent with Cropanzano, Byrne, Bobocel, and Rupp's (2001) model of "event vs. entity" justice judgments
Validated measure of organizational justice
Colquitt (2001): Procedural, Distributive, Informational and Interpersonal. Confirmatory factor analyses in two independent samples showed that a four-factor structure fit the data significantly better than one, two, or three-factor versions. In addition, structural equation modeling results revealed that the four justice dimensions had unique relationships with various outcome measures.
Few Examples of Latent Model Approach to Justice
Colquitt and Shaw's (2005) and Liao's (2007) results showed that the four justice dimensions had strong loadings on a higher-order organizational justice factor. In addition, because the specific dimensions were measured and included in the study as indicators, the reader could peruse the correlation matrix to examine any differential relationships that might be evident.
Organizational Justice
Comprised of Distributive, Procedural, Interactional, Interpersonal and maybe Informational Justice Greenberg (1987). Extent to which employees perceive they are being treated fairly
Overall Fairness Measure Benefits
Different Perspective Distributive, procedural, interpersonal, and informational justice serve as antecedents of overall fairness, with overall fairness then serving as an antecedent of attitudinal and behavioral outcomes (Ambrose & Arnaud, 2005; Ambrose & Schminke, 2009; Colquitt, Greenberg, & Scott, 2005; Colquitt & Shaw, 2005; Leventhal, 1980) Allows scholars to verify that it is that sense of fairness or unfairness that explains why distributive, procedural, interpersonal, and informational justice are predictive of key organizational outcomes. The relationship between overall fairness and those outcomes is also devoid of multicollinearity Because of its brevity, overall fairness could serve as a useful construct for inclusion in studies that are not focused on organizational justice per se.
Distributive Justice
Distributive justice has been defined as the degree to which the appropriate allocation norm is followed in a given decision-making context. Be it equity or equality The fairness of decision outcomes. Individuals react to outcome allocations by comparing their ratio of outcomes to inputs to some relevant comparison other. If those ratios match, the individual feels a sense of equity. Homans (1961), Adams (1965)
Justice is Treated as an Exogenous Variable
Due to reactive focus Scales like Colquitt's (2001) or Moorman's (1991) are utilized as the independent variables, with direct and indirect effects on attitudinal, affective, and behavioral variables. Because of this little about the circumstances that result in an adherence to the justice rules. Proactive treats justice as a motive
Emotions vs Moods vs Trait Affectivity
Emotions are short-term feeling states that are referenced to a particular target. Moods are feeling states that are weaker in intensity and longer in duration than emotions, and lack a salient target. Trait affectivity reflects a dispositional tendency to experience positive or negative feeling states.
Fairness has indirect effects on behavior
Fairness has indirect effects on behavior via instrumental mediators like perceptions of social exchange (e.g., Colquitt et al., 2013), relational mediators like social identity (e.g., Johnson & Lord, 2010), and affect-imbued morality mediators like anger (e.g., Weiss, Suckow, & Cropanzano, 1999).
Fairness
Fairness is valued for instrumental reasons (e.g., it reduces uncertainty and fears of exploitation) relational reasons (e.g., it communicates positive social worth) moral reasons (e.g., it aligns with normative standards of ethical conduct).
Procedural Justice
Fairness of decision-making processes. Thibaut and Walker (1975) argued that procedures were viewed as fair when disputants possessed process control, meaning that they could voice their concerns in an effort to influence the decision outcome Leventhal (1980) argued that allocation procedures would be viewed as fair when they adhered to several "rules", including consistency, bias suppression, accuracy, correctability, and ethicality.
Interactional Justice
Fairness of interpersonal interactions Bies and Moag (1986). Interactional justice is fostered when relevant authorities communicated procedural details in a respectful and proper manner, and justified decisions using honest and truthful information.
Fairness Theory
Fairness theory argues that individuals react to decision events by engaging in counterfactual thinking (Folger & Cropanzano, 1998, 2001). 1. "Could" counterfactuals consider whether the decision event could have played out differently. 2. "Should" counterfactuals consider whether moral standards were violated during the event. 3. "Would" counterfactuals consider whether one's well-being would have been better if events had played out differently. The theory suggests that individuals will blame an authority for an event when it could have (and should have) occurred differently, and when well-being would have been better had the alternative scenario played out. (Shaw, Wild, & Colquitt, 2003) Below Distributive justice is most relevant to the "would" counterfactual because well-being is often defined in outcome terms. Procedural and interpersonal concepts such as bias suppression, ethicality, and propriety are most relevant to the "should" counterfactual because they are more "morally charged." Informational justice is most relevant to the "could" counterfactual if explanations are used to excuse the event in question.
Instrumental Model of Justice
For the Instrumental model (Lind & Tyler, 1988), Thibaut and Walker (1975) argued that justice is valued because it provides a sense of control and predictability for outcomes over the long term. From this perspective, individuals value and consider justice rules because justice is instrumental—it helps in the attainment of valued outcomes.
Goldman (2003)
Goldman (2003) examined this issue in a study of reactions to a termination event. The results of the study showed that anger was predicted by a three-way interaction of distributive justice, procedural justice, and interactional justice. The pattern of this interaction revealed that the procedural and interactional justice combinations had stronger relationships with anger when distributive justice was low than when it was high. In addition, the highest levels of anger were felt when all three justice dimensions were low. The results revealed the same three-way interaction for legal claiming that was described above, and the results further showed that the effect was mediated by anger. The results showed that the three-way interaction for legal claiming was more pronounced for individuals who experience anger more frequently, as a function of their disposition.
Judge et al., 2006
Judge et al. (2006) surveyed employees at the end of each workday for a three week time period. The participants completed self-report measures of interpersonal justice, anger, job satisfaction, and counterproductive behavior each day, and a significant other completed a measure of trait anger. The ESM results showed that more than half of the variation in counterproductive behavior was within-person variance (as opposed to between-person variance). The results suggested that the relationship between interpersonal justice and counterproductive behavior was mediated by state anger and job satisfaction. Moreover, trait anger moderated the interpersonal justice-state anger relationship, such that the linkage was stronger for individuals who were more prone to experience feelings of anger and hostility.
Research with Overall Fairness as Mediator
Kim and Leung's (2007) results suggested that overall fairness mediated the effects of the specific justice dimensions on job satisfaction and turnover intentions. Ambrose and Schminke's (2009) results revealed the same pattern for both attitudinal and behavioral outcomes, including citizenship and counterproductive behavior.
Individual Difference Variables Influencing Justice
Korsgaard, Roberson, and Rymph (1998) thought that assertive employees would receive more extensive justifications from their managers because of their tendency to use confident posture and eye contact and to ask questions of clarification. A laboratory study supported the relationship between employee assertiveness and adherence to informational justice rules, but the linkage was not supported in a field study. Scott et al. (2007) thought that charismatic employees have a "personal magnetism" that inspires affective responses on the part of their managers. Those affective responses were operationalized using positive and negative sentiments (i.e., tendencies to experience positive or negative emotions when around specific individuals). positive sentiments would prompt managers to be more courteous and friendly to employees and engage in more frequent instances of information sharing with them. Indeed, a field study of insurance company employees revealed a significant relationship between charisma and interpersonal justice, with both positive and negative sentiments mediating that relationship. Contrary to expectations, no relationship was observed for informational justice.
What is a Latent Model? Relevance to Org Justice
Law, Wong, & Mobley, 1998; Wong, Law, & Huang, 2008 Latent Model: Construct is viewed as a higher-order, unobservable abstraction underlying the specific dimensions. Specific dimensions serve as different manifestations or realizations of the construct, with each representing the construct with varying degrees of accuracy. The specific dimensions tend to be functionally similar and more or less substitutable. Moreover, the specific dimensions are highly correlated, as the latent construct is defined solely by the common variance shared by the dimensions Must have a theoretical reason for using this. If predictions are focused on the independent or interactive effects of specific justice dimensions, such an approach is obviously inappropriate. If predictions are focused on the effects of shared justice variance, however, then a latent model approach would be suitable (Fassina et al., 2008).
Focus Matching Org Justice Results
Match = supervisor originating justice predicting supervisor directed citizenship Non match = supervisor originating justice predicting organization diirected citizenship Rupp and Cropanzano (2002) and Horvath and Andrews (2007) examined procedural and interpersonal justice whereas Liao and Rupp (2005) included procedural, interpersonal, and informational justice. Taken together, the correlation matrices in the three studies yielded 28 different matching vs. nonmatching contrasts. Of those, 18 contrasts revealed the predicted pattern. Interestingly, all three studies suggested that supervisor-originating justice (whether procedural, interpersonal, or informational) was actually a stronger predictor of organization-directed citizenship than was organization-originating justice. Indeed, supervisor-originating justice always explained more variance in the citizenship outcomes, regardless of their target, than organization-originating justice.
Early Research Differentiating Interactional Justice from Others
Moorman's (1991) results showed that interactional justice was distinct from procedural justice in a confirmatory factor analysis. It was also distinct from a measure of distributive justice taken from Price and Mueller's (1986) work. MOORMAN (1991) Study From a predictive validity perspective, the results also showed that interactional justice was a better predictor of citizenship behaviors than either procedural or distributive justice. Introduced a measure of interactional and procedural justice and established citizenship behaviors as outcome of justice Some scholars who utilized Moorman's (1991) scale sometimes wound up combining the interactional and procedural dimensions due to high intercorrelations (e.g., Mansour-Cole & Scott, 1998; Skarlicki & Latham, 1997).
Issues with Differentiating Justice Facets
Multicolinearity and Decreased Parsimony Multicollinearity- multicollinearity inflates the standard errors around regression coefficients, harming statistical power and resulting in "bouncing betas" from one study to the next (Cohen, Cohen, West, & Aiken, 2003; Schwab, 2005). Moreover, because the formula for beta subtracts some portion of predictor covariation from a given correlation, multicollinearity results in circumstances where a given predictor's beta can be near-zero, or even opposite in sign from its correlation. Finally, shared covariance between a set of predictors and an outcome creates interpretational difficulties given that no one predictor receives "credit" for the effect. Studies using Colquitt's (2001) scales to measure the justice dimensions tend to yield: Distributive-procedural correlations in the .50's, Procedural-informational correlations in the .60's Interpersonal-informational correlations in the .60's, with other correlations tending to fall in the .40 range (e.g., Ambrose et al., 2007; Bell et al., 2006; Camerman et al., 2007; Choi, 2008; Jawahar, 2007; Johnson, Selenta, & Lord, 2006; Judge & Colquitt, 2004; Mayer et al., 2007; Roberson & Stewart, 2006; Scott et al., 2007; Siers, 2007; Spell & Arnold, 2007; Streicher et al., 2008). Studies using multifoci justice scales tend to yield "within-focus correlations" (e.g., supervisor-originating procedural justice with supervisor-originating interpersonal justice) in the .70's, with other correlations falling in the .40 area (Blader & Tyler, 2003) meta-analyses place even the distributive-procedural justice correlation in the .50-.60 range (Cohen-Charash & Spector, 2001; Colquitt et al., 2001; Hauenstein, McGonigle, & Flinder, 2001).
Early Research on Justice, Differentiating Procedural from Distributive
Procedural justice variables were stronger predictors of organizational commitment and trust in supervisor, whereas the distributive justice and outcome favorability variables were stronger predictors of pay satisfaction Folger and Konovsky (1989) This pattern—where procedural justice was a stronger predictor of system-referenced attitudes and distributive justice was a stronger predictor of outcome-referenced attitudes, came to be termed the "two-factor model" (Sweeney & McFarlin, 1993, see also McFarlin & Sweeney, 1992).
Overall Fairness as Antecedant
Rodell and Colquitt (2008) cast overall fairness as an antecedent of the specific justice dimensions, consistent with Lind's (2001a) description of fairness heuristic theory. As in the discussion of the latent model, such variations reveal the differing predictions that justice theories make for overall assessments of fairness.
Organizational Antecedents of Justice
Schminke, Ambrose, and Cropanzano (2000) linked aspects of the organization's structure to adherence to the process control and bias suppression rules of procedural justice. For example, employees reported less adherence to those rules when the organization had a centralized authority hierarchy, meaning that even small decisions had to be referred to a "higher up" for approval. Gilliland and Schepers (2003) conducted a survey of human resources managers in a study of adherence to interpersonal and informational justice rules during layoff events. One aspect of the organization, whether or not it was unionized, predicted the number of days notice that employees were given—an aspect of informational justice. That variable was not related to the amount of information that was shared, however, nor was it related to the demeanor used to communicate the layoff—an aspect of interpersonal justice. Mayer et al., (2007) However, the results seemed to support a negative relationship between neuroticism and interpersonal justice rule adherence and a positive relationship between agreeableness and informational justice rule adherence. Neurotic managers tended to communicate less respectfully, and agreeable managers tended to be more candid and forthcoming. None of the Big Five variables predicted adherence to procedural justice rules, however.
Distinctions Between Informational and Interpersonal Justice
Several of the studies that have utilized Colquitt's (2001) scales have provided factor-analytic support for the interpersonal-informational distinction (e.g., Ambrose, Hess, & Ganesan, 2007; Bell, Wiechmann, & Ryan, 2006; Camerman, Cropanzano, & Vandenberghe, 2007; Choi, 2008; Jawahar, 2007; Judge & Colquitt, 2004; Mayer, Nishii, Schneider, & Goldstein, 2007; Scott, Colquitt, & Zapata-Phelan, 2007; Streicher et al., 2008). Of course, several other studies have only included one of the interactional facets, depending on which is most relevant to the research question. Roberson and Stewart's (2006) study of the motivational effects of feedback focused on informational justice but not interpersonal justice. As another example, Judge, Scott, and Ilies's (2006) study of hostility and deviance focused on interpersonal justice but not informational justice
The Deontic/Moral Virtue Model of Justice
The deontic model (Cropanzano, Goldman, & Folger, 2003; Folger, 1998, 2001), sometimes also termed the moral virtue model (Cropanzano et al., 2001) argues that individuals attend to justice issues because it signals a respect for principled moral obligations. That is, rather than merely signaling a sense of control or esteem, justice is valued because "virtue serves as it's own reward" (Folger, 1998, p. 32). The deontic model is able to explain why individuals value justice even when it does not benefit their own outcomes, and even when it does not improve their standing in some relevant social group (Turillo, Folger, Lavelle, Umphress, & Gee, 2002).
The Relational Model of Justice
The relational model argues that individuals are motivated to belong to groups and that they look for signals about the extent to which those groups value them (Lind & Tyler, 1988; Tyler & Lind, 1992). When authorities are neutral and unbiased, or when they implement procedures with dignity and respect, they convey to the relevant individuals a sense of status in the group. This model is capable of explaining why fair treatment is associated with more favorable reactions even when it does not enhance actual control over outcomes or resources (Tyler, 1994).
Fox, Spector and Miles (2001)
The results showed that negative affective well-being (an amalgam of fear, anger, disgust, and sadness) mediated the relationship between procedural justice and counterproductive behaviors. Positive affective well-being (an amalgam of enthusiasm, pride, happiness, and contentment) was also included, though it exhibited weaker relationships with counterproductive behaviors. Fox et al. (2001) also included two measures of trait affectivity—trait anger and trait anxiety—but neither was shown to be a significant moderator of the justice-counterproductive behavior relationships.
Fairness Heuristic Theory
This theory argues that newcomers in an organization are motivated to form a "fairness heuristic" quickly, so that the heuristic can be used to inform decisions about whether to cooperate with authorities (Lind, 2001a; Van den Bos, 2001). The newcomers draw on whatever justice-relevant information is first encountered or most interpretable, regardless of whether it is of a procedural, distributive, interpersonal, or informational nature. During this initial judgmental phase, the justice-relevant information is used to form a general fairness impression. However, after this initial phase it is actually that general impression that drives judgments of the specific justice dimensions (Lind, 2001a). At that point, judgments of procedural, distributive, interpersonal, or informational justice merely serve as different manifestations of the same fairness heuristic construct.
Research on Mood and Justice
Van den Bos (2003) examined the effects of mood on fairness perceptions when information on justice criteria was clear vs. unclear. In two studies, the author manipulated the degree to which Thibaut and Walker's (1975) process control criterion was clearly and unambiguously fulfilled by comparing three conditions: (a) process control explicitly granted, (b) process control explicitly denied, and (c) process control not mentioned. The author also manipulated mood by asking participants to describe and write about the experience of being either happy or angry. The results of the study showed that mood had little impact on fairness perceptions when information about process control was clear and unambiguous. However, when information on that procedural justice rule was omitted, participants in the positive mood condition perceived more fairness than participants in the negative mood condition. Essentially, mood "filled in the gaps" left by the absence of clear information on relevant justice rules.
Affect as Outcome of Justice
Weiss et al.'s (1999) results showed that happiness was driven solely by outcome favorability, suggesting that it depends only on primary appraisals of harm or benefit. The authors had expected that pride would be highest when the outcome was favorable and the procedure was either just or unfavorably biased. However, as with happiness, pride was driven primarily by outcome favorability, with the predicted procedural pattern failing to emerge. With respect to the negative emotions, anger was highest when the outcome was unfavorable and the procedure was unfavorably biased. Guilt, in contrast, was highest when the outcome was favorable and the procedure was favorably biased. Krehbiel and Cropanzano (2000) replicated the above findings for happiness and pride while also showing that a negative emotion—disappointment—was solely driven by outcome favorability. Barclay, Skarlicki, and Pugh (2005) conducted a field study of reactions to a layoff event, focusing primarily on the negative emotions of guilt and anger. Their results showed that those emotions were an interactive function of outcome favorability (i.e., the quality of the severance package) and the justice of the layoff process (i.e., procedural justice and interactional justice).
