Journal of Applied Psychology
2003, Vol. 88, No. 3, 444 – 458
Copyright 2003 by the American Psychological Association, Inc.
0021-9010/03/$12.00 DOI: 10.1037/0021-9010.88.3.444
To Justify or Excuse?: A Meta-Analytic Review
of the Effects of Explanations
John C. Shaw, Eric Wild, and Jason A. Colquitt
University of Florida
The authors used R. Folger and R. Cropanzano’s (1998, 2001) fairness theory to derive predictions about
the effects of explanation provision and explanation adequacy on justice judgments and cooperation,
retaliation, and withdrawal responses. The authors also used the theory to identify potential moderators
of those effects, including the type of explanation (justification vs. excuse), outcome favorability, and
study context. The authors’ predictions were tested by using meta-analyses of 54 independent samples.
The results showed strong effects of explanations on both the justice and response variables. Moreover,
explanations were more beneficial when they took the form of excuses rather than justifications, when
they were given after unfavorable outcomes, and when they were given in contexts with instrumental,
relational, and moral implications.
Recent magazine and newspaper headlines have demonstrated
the tendency for organizations to withhold adequate explanations.
Consider the recent layoffs at Dell Computer, a company rated as
one of Fortune magazine’s “100 Best Companies to Work For”
(Levering & Moskowitz, 2000). Like many companies, Dell has
reacted to the recent economic downturn with a series of job and
pay cuts. A recent Time magazine article illustrated how one layoff
event was handled (Cohen & Thomas, 2001). An e-mail instructed
employees to attend a meeting at a nearby hotel, where they found
a set of managers whom they had never met. The authors wrote:
“The quarrel is a very pretty quarrel as it stands; we should only spoil
it by trying to explain it.”
—Richard Brinsley Sheridan from The Rivals, act IV, sc. iii
“I wish he would explain his explanation.”
—Lord Byron in Don Juan, Dedication, st. 2
Organizational life is rife with decisions that affect the interests
of employees (Sitkin & Bies, 1993). For example, management
might inform the organization of imminent layoffs, announce a
merger with another firm, or institute a new drug-testing policy.
Within the organization, a supervisor could approve a subordinate’s budget request, deny a request for a salary increase, or
provide a lower-than-expected performance evaluation. Each of
these events would seem to demand some sort of explanation,
particularly when the decision was unfavorable or somehow
controversial.
Unfortunately, many organizations fail to supply explanations
for key events or provide explanations that are so vague that they
lack real information (Folger & Skarlicki, 2001; Smeltzer & Zener,
1992). In a recent review, Folger and Skarlicki (2001) attempted to
explain “why tough times can lead to bad management” (p. 97).
The authors argued that managers distance themselves from employees who receive bad news and fail to provide adequate explanations as a result. This distancing can occur for several reasons,
including emotional distress or fear of being blamed for a negative
outcome. It may also result from apprehension about triggering a
wrongful termination or discrimination lawsuit.
The bosses stuck to their script. The economy was bad. We can’t
afford to keep you. So we’re not. Hand in your badges on the way out.
There were no individual explanations for why these workers— out of
a work force of 40,000 — had been picked. The members of the firing
squad never introduced themselves. It was over in eight minutes.
(p. 38)
Even though it is logical for organizations to be concerned about
revealing confidential information or triggering a damaging lawsuit, the failure to give an explanation— or the use of an inadequate
one— could have adverse consequences. Academic research has
linked the absence of adequate explanations to decreased levels of
cooperation (e.g., task and citizenship behaviors; Colquitt, 2001;
Konovsky & Cropanzano, 1991), increased levels of retaliation
(e.g., litigation intentions, theft; Greenberg, 1990; Wanberg,
Bunce, & Gavin, 1999), and increased levels of withdrawal (e.g.,
turnover intentions; Ball, Trevino, & Sims, 1993; Konovsky &
Cropanzano, 1991). Much of this research has measured or manipulated variation in explanation provision, defined here as the
extent to which an explanation is given for a decision. Other
research has measured or manipulated variation in explanation
adequacy, defined as the extent to which provided explanations are
clear, reasonable, and detailed.
The research cited previously has shown that adequate explanations can have beneficial effects, but the explanations literature
remains unclear on three key issues. First, the size of explanation
effects has been inconsistent; some studies have reported strong
zero-order effects (e.g., Gilliland & Beckstein, 1996; Greenberg,
John C. Shaw, Eric Wild, and Jason A. Colquitt, Department of Management, University of Florida.
An earlier version of this work was presented in J. Greenberg and J. A.
Colquitt (Chairs), Emerging contexts for organizational justice, a symposium conducted at the annual meeting of the Academy of Management,
Denver, Colorado, August 2002.
Correspondence concerning this article should be addressed to John C.
Shaw, Department of Management, Warrington College of Business Administration, University of Florida, P.O. Box 117165, Gainesville, Florida
32611-7165. E-mail: [email protected]
444
THE EFFECTS OF EXPLANATIONS
1990), whereas others have reported nonsignificant ones (e.g.,
Gilliland, 1994; Schaubroeck, May, & Brown, 1994). Second, the
merits of different types of explanations remain uncertain. Some
studies have shown that excuses, where the authority shifts responsibility to some external cause, can have beneficial results (e.g.,
Crant & Bateman, 1993; Tata, 2000). Others have suggested that
justifications, which admit responsibility while appealing to higher
order concerns, are more effective (Bobocel & Farrell, 1996;
Conlon & Murray, 1996).
Finally, research on where and when to provide adequate explanations has proven inconclusive. Some studies have predicted
that explanations are more beneficial in light of unfavorable outcomes, but the results have been inconsistent (e.g., Colquitt &
Chertkoff, 2002; Gilliland, 1994; Ployhart, Ryan, & Bennett, 1999;
Schaubroeck et al., 1994). Moreover, some studies have linked
explanations to key outcomes in intuitively important contexts,
such as layoffs or evaluation procedures (e.g., Cobb, Vest, & Hills,
1997; Wanberg et al., 1999), whereas others have shown only
marginal effects in those settings (e.g., Mellor, 1992; Rousseau &
Tijoriwala, 1999; Skarlicki, Ellard, & Kelln, 1998).
The purpose of this study was to conduct a meta-analytic review
of the explanations literature. In so doing, we structured our review
around the propositions of fairness theory—a recently introduced
integrative theory of organizational justice (Folger & Cropanzano,
1998, 2001). Fairness theory is particularly relevant to the study of
explanations and can provide a useful framework for examining
their effects. The theory was recently applied to the study of
explanations by Gilliland, Groth, Baker, Dew, Polly, and Langdon
(2001) and Colquitt and Chertkoff (2002). Both studies illustrated
that fairness theory can be useful in deriving predictions regarding
how, where, and when explanations should be most effective. In
this article, we provide a brief overview of the explanations literature and discuss the basic components of fairness theory. We then
use fairness theory to structure our specific hypotheses.
Overview of the Explanations Literature
Interest in explanations can be traced back to several early
works in psychology, sociology, and philosophy (for a review, see
Schlenker, 1980). According to Merriam-Webster’s Collegiate
Dictionary (1998), an explanation is the act or process of “making
something clear or understandable.” The term implies revealing
the reason for, or the cause of, some event that is not immediately
obvious or entirely known. Much of the scholarly interest in
explanations may have been triggered by Scott and Lyman’s
(1968) general purpose taxonomy of explanations. They distinguished between two types of explanations: excuses and
justifications.
Excuses are defined as explanations in which the decision maker
admits that the act in question is unfavorable or inappropriate but
denies full responsibility by citing some external cause or mitigating circumstance. For example, a soldier might admit to having
killed other people, an immoral act, but explain that he or she was
following orders and therefore was not fully responsible (Scott &
Lyman, 1968). With justifications, the decision maker accepts full
responsibility but denies that the act in question is inappropriate by
pointing to the fulfillment of some superordinate goal. For example, a soldier might justify killing others by asserting that his or her
side is fighting for the cause of freedom (Scott & Lyman, 1968).
445
Over the past three decades, researchers have further refined
Scott and Lyman’s (1968) taxonomy (Bies, 1987b; Bies, 1989;
Schlenker, 1980; Schönbach, 1990; Sitkin & Bies, 1993; Tedeschi
& Reiss, 1981). Schlenker’s (1980) work further clarified the
nature of both excuses and justifications by noting that excuses can
point to either unforeseen consequences or extenuating circumstances, while justifications can draw on relevant social comparisons or higher order goals. Tedeschi and Reiss (1981) argued that
excuses can point to a lack of intention, planning, capacity, or
volition, and justifications can appeal to a higher authority, ideology, norms, or loyalties. Finally, work by Bies applied new terms
to the excuse and justification concepts (Bies, 1987b; Bies, 1989;
Sitkin & Bies, 1993). He termed excuses as causal accounts or
mitigating accounts, while justifications were labeled ideological
accounts or exonerating accounts.
The literature on explanations is several decades old, but a
chapter by Bies and Moag (1986) created a renewed interest in the
concept. The authors characterized an allocation decision as a
sequence of three events: (a) the following of a procedure; (b) an
interaction between the allocator and allocation recipient(s), and
(c) the allocation of the outcome. In so doing, Bies and Moag
(1986) called attention to the interpersonal communication process, which had been neglected in previous research in the organizational justice literature. They also identified specific principles
that promote fairness during the interaction phase. One of those
principles is the provision of an adequate explanation.
Since Bies and Moag’s (1986) discussion of explanations, scholars have used explanation provision as well as explanation adequacy to predict a variety of outcomes. Most commonly, explanations have been used to predict the perceived fairness of decision
outcomes (i.e., distributive justice) and the perceived fairness of
decision-making processes (i.e., procedural justice). This last outcome is particularly common, given that justice scholars have
argued that explanations are a critical aspect of proper procedural
enactment (Bies & Moag, 1986; Folger & Bies, 1989; Tyler &
Bies, 1990). Some studies have shown that explanations improve
such justice judgments (e.g., Bies & Shapiro, 1988; Gilliland &
Beckstein, 1996; Greenberg, 1990; Shapiro, 1991), whereas others
have yielded nonsignificant results (e.g., Colquitt & Chertkoff,
2002; Gilliland, 1994; Schaubroeck et al., 1994).
Other studies have connected explanation provision and adequacy to more general responses to the decision-making event. For
example, many scholars have examined the effects of explanations
on cooperation responses. Tyler and Blader (2000) defined cooperation responses as those that “act to promote the goals of the
group” (p. 3). Some of these responses may be in-role, such as task
motivation, performance, or refusal to work for competing groups.
Others may be extra-role, such as citizenship behavior or demonstration of particularly strong levels of loyalty. In contrast, other
studies have focused on retaliation responses. Skarlicki and Folger
(1997) defined retaliation responses as “adverse reactions to perceived unfairness by disgruntled employees toward their employer” (p. 434). Some of these responses may be very active
responses, such as theft or complaints, whereas others may be
passive, such as anger, blame, or stress. Some studies have shown
that explanations promote cooperation or reduce retaliation (e.g.,
Colquitt, 2001; Gilliland, 1994; Greenberg, 1990; Konovsky &
Folger, 1991; Schweiger & DeNisi, 1991), whereas others have
failed to show significant effects (e.g., Bies, Shapiro, & Cum-
SHAW, WILD, AND COLQUITT
446
mings, 1988; Brockner, DeWitt, Grover, & Reed, 1990; Colquitt &
Chertkoff, 2002).
Finally, still other scholars have linked explanations to withdrawal responses. Hulin (1991) defined withdrawal responses as
“the set of behaviors that dissatisfied individuals enact to avoid the
work situation” (p. 476). Most of the responses examined by past
researchers comprise withdrawal from an existing organizational
relationship (e.g., turnover intentions, absenteeism). However,
other forms of withdrawal serve to prevent a relationship from
even developing. For example, an individual may possess low
application intentions regarding a job opening, or a customer may
refuse to engage in further business with an organization. As with
the outcomes discussed earlier, some studies have shown that
explanations reduce withdrawal (e.g., Ball et al., 1993; Conlon &
Murray, 1996); others have yielded nonsignificant results (e.g.,
Rousseau & Tijoriwala, 1999; Schaubroeck et al., 1994).
Application of Fairness Theory
Several theories, models, and frameworks can provide insights
into what the effects of explanations should be and where and
when they should be strongest or weakest. These include attribution theory (e.g., Wong & Weiner, 1981), work on the interactive
effects of outcomes and procedures (e.g., Brockner & Wiesenfeld,
1996), and discussions of interactional justice (e.g., Bies & Moag,
1986). These works could help ground a subset of the predictions
tested in our meta-analysis, but we are aware of only one theory
that is relevant to the entire set. This theory, first introduced in
Folger and Cropanzano (1998), is fairness theory. It is a reconceptualization of an earlier theory called referent cognitions theory
(Folger, 1986a, 1986b, 1987, 1993).
Fairness theory posits that the assignment of blame or accountability is a necessary step in reacting to decision-making events
(Folger & Cropanzano, 1998, 2001). It therefore describes the
process by which individuals go about forming perceptions of
blame. In particular, the theory argues that individuals assign
blame by comparing what happened to what might have been
(Folger & Cropanzano, 1998, 2001). These mental simulations of
other possible events are referred to as counterfactuals because
they literally are contrary to the facts.
There are two kinds of counterfactuals that interact to determine
blame or accountability: could counterfactuals and should counterfactuals. Could counterfactuals compare what the decision
maker did to what the decision maker could have done (Folger &
Cropanzano, 1998, 2001). When people engage in this type of
counterfactual thinking, they are trying to decide if there were
other feasible alternatives within the discretion of the decision
maker. According to fairness theory, if the individual determines
that there were other feasible options over which the decision
maker had control, then it is possible to assign blame (Folger &
Cropanzano, 1998, 2001).
The process of should counterfactual thinking forms the second
half of the accountability judgment. Should counterfactuals compare what the decision maker did to what he or she should have
done, from an ethical perspective (Folger & Cropanzano, 1998,
2001). This type of counterfactual therefore concerns the evaluation of good versus bad and right versus wrong. According to the
theory, for an event to be unfair, it must violate the ethical
standards to which people are expected to adhere (Folger &
Cropanzano, 1998, 2001).
Fairness theory also describes the process individuals use to
judge the impact of the events that affect their lives. Specifically,
would counterfactuals compare a person’s current state of wellbeing with mental simulations of other, perhaps more positive,
states (Folger & Cropanzano, 1998, 2001). The theory argues that
individuals will evaluate an event as having less negative impact
when the current state of well-being is similar to other imagined
states.
Figure 1 illustrates how the three counterfactuals interact. The
reciprocal arrows indicate that the formation of the counterfactuals
can, theoretically, occur in any order (Folger & Cropanzano, 1998,
2001). The arrows also illustrate that all three counterfactuals must
be activated for an individual to perceive an injustice. As an
example, if a person sees an event as favorable (thereby failing to
activate the would counterfactual), there is little reason to imagine
what the decision maker could have or should have done differently. Without negative impact, there is no reason to consider
blame (and vice versa).
Fairness theory is uniquely suited to describing why explanations should have beneficial effects. From the perspective of the
theory, explanations have the potential to deactivate both the could
and the should counterfactuals (see Figure 1). The act of providing
an excuse can demonstrate that some external cause or mitigating
circumstances made the decision necessary or unavoidable. The
more adequate the excuse is, the more the recipient will see the
event in question as the only feasible option. Thus, excuse provision and adequacy can impact the could counterfactual, breaking
the could–should chain that determines blame and accountability.
Similarly, the act of providing a justification can demonstrate
that the decision was appropriate in light of some superordinate
goal. The more adequate the justification is, the more the recipient
will see the event in question as ethically defensible. Thus, justification provision and adequacy affect the should counterfactual
and again prevent an injustice from being perceived. The fact that
justifications and excuses affect different mechanisms illustrates
the utility of fairness theory in an explanations context. The theory
is capable of illustrating exactly why two different forms of
explanations can be beneficial.
We, therefore, made the following predictions for explanation
provision and explanation adequacy. Note that our hypotheses use
the term beneficial effects for simplicity, meaning positive effects
on procedural justice, distributive justice, and cooperation but
negative effects on retaliation and withdrawal.
Hypothesis 1: Explanation provision (whether excuse or justification) will have beneficial effects on procedural and distributive justice and cooperation, retaliation, and withdrawal
responses.
Hypothesis 2: Explanation adequacy (whether excuse or justification) will have beneficial effects on procedural and distributive justice and cooperation, retaliation, and withdrawal
responses.
Moderators of Explanation Effects
Our first two hypotheses predicted beneficial main effects for
explanation provision and adequacy, but it is clear that existing
THE EFFECTS OF EXPLANATIONS
Figure 1.
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The three counterfactuals in fairness theory.
studies have yielded inconsistent results. As noted earlier, some
studies have yielded strong effects of explanations on justice
judgments and other responses (e.g., Bies & Shapiro, 1988; Conlon & Murray, 1996; Gilliland & Beckstein, 1996; Greenberg,
1990; Shapiro, 1991), and other studies have not (e.g., Brockner et
al., 1990; Colquitt & Chertkoff, 2002; Gilliland, 1994; Schaubroeck et al., 1994). What can explain these inconsistencies? One
obvious possibility is sampling error, but it seems likely that some
moderating variables could be altering the strength of explanation
effects.
The identification of such moderators is important practically
and theoretically. Practically speaking, we noted that many organizations are often reluctant to offer explanations because of
concerns over potential litigation or for the protection of confidential information. An examination of relevant moderators can show
when that practice is likely to be most damaging. Theoretically
speaking, moderators answer the critical “who-where-when” questions needed for sound theory development (Whetten, 1989). As
Campbell (1990) notes, often the goal of a scientist should not be
to test whether a hypothesis is supported or refuted but rather to
determine the conditions under which a relationships holds.
We, therefore, used fairness theory to identify specific moderators that could alter the strength of explanation effects. Each of
the moderators is predicted to have some impact on the counterfactual mechanisms contained within the theory. These moderators
took the form of substantive characteristics of the studies included
in our meta-analysis and are summarized in the Appendix. Three
specific moderators were examined in this review: the type of
explanation, outcome favorability, and the context in which the
decision-making event occurred.
Type of Explanation
Hypotheses 1 and 2 predicted beneficial effects for explanations,
regardless of whether the explanations take the form of excuses or
justifications. However, it is certainly important to examine
whether one type of explanation is more effective than the other.
As shown in the Appendix, several studies in the existing literature
have examined one specific type of explanation— either excuses or
justifications. Other studies have varied the type of explanation
within the study by manipulating it across experimental conditions.
Still other studies have used self-report measures of explanations
without specifying excuses or justifications, which creates unmeasured variance in explanation type. For example, Brockner,
Konovsky, Cooper-Schneider, Folger, Martin, and Bies (1994)
used this survey item: “My supervisor carefully explained to me
why I was laid off” (p. 401). Some participants could be recalling
justifications while responding to this item, whereas others could
be recalling excuses.
Of the studies with classifiable explanations, which should yield
stronger effects: those using excuses or those using justifications?
The studies that manipulated both justifications and excuses within
a single study can provide insights into this question. We are aware
of six such studies. Three of those studies found excuses to have
more beneficial effects on retaliation responses (assignment of
blame, perceived need for disciplinary actions) and withdrawal
SHAW, WILD, AND COLQUITT
448
responses (time taken to return for a subsequent experiment; Crant
& Bateman, 1993; Gonzales, 1992; Tata, 2000). Two other studies
have found opposite results, with justifications having more beneficial effects on justice perceptions and perceptions of explanation adequacy (Bobocel & Farrell, 1996; Conlon & Ross, 1997).
The sixth study, by Gilliland et al. (2001), used fairness theory
to frame its predictions. The authors termed excuses could reducing explanations and justifications should reducing explanations.
Their first study included a should reducing explanation; the
results failed to illustrate main effects on procedural justice, distributive justice, or cooperative responses (recommendation intentions). Their second study included a could reducing explanation,
which did in fact improve procedural justice and cooperative
responses (recommendation intentions). Their third study included
both types of explanations, with the two yielding similar results.
Although the studies cited previously are split on the issue,
various aspects of Folger and Cropanzano’s (1998) discussion of
fairness theory suggest stronger effects for excuses than justifications. After describing the could counterfactual in terms of feasible
options and discretionary conduct, the authors described the should
counterfactual as a “key basis for linking people’s discretionary
conduct [italics added] with the consequences of that conduct” (p.
188). Similarly, they described should counterfactuals as “morally
superior alternatives in the feasible set [italics added]” (p. 189).
Although the authors argue that the counterfactuals may be considered in any order, their discussion implies that should issues
will not even be explored if the could counterfactual is not
activated.
If should counterfactuals only consider feasible actions, then
excuses should be more powerful than justifications. Specifically,
excuses have the power to deactivate both the could counterfactual
(by directly demonstrating a lack of feasible options) and the
should counterfactual (by indirectly preventing even the consideration of moral standards). Justifications are incapable of deactivating both counterfactuals, because they admit— by definition—
that other feasible options were possible (though less morally
appropriate). Thus, the mechanisms discussed in fairness theory
are uniquely capable of differentiating the power of excuse and
justification effects.
Finally, we should note that Schlenker’s (1980) discussion of
explanations provides more support for the powerful effects of
excuses. Schlenker (1980) emphasized the importance of being
directly responsible for a decision event by writing, “responsibility
is the adhesive that links an actor to an event and attaches an
appropriate sanction to the actor who deserves it” (p. 126). When
that adhesive is removed, no consideration of the justifiability of
an event is necessary. We therefore predicted:
Hypothesis 3: Explanations will have more beneficial effects
when they take the form of excuses rather than justifications.
Outcome Favorability
In addition to type of explanation, it is important to examine
whether outcome favorability alters the strength of explanation
effects. As shown in the Appendix, several studies in the extant
literature have used uniformly low outcome favorability (e.g.,
examining individuals rejected for a job or denied a resource
request). Several other studies have varied outcome favorability. In
these studies, outcome favorability was varied by manipulating it
across experimental conditions or by using self-report measures to
assess passive variance in it. It is important to note that no study
has ever used uniformly high outcome favorability.
Which studies should yield stronger effects: those using low
outcome favorability or those using varying outcome favorability?
The studies that manipulated outcome favorability can provide
insights into this question. Several of those studies have illustrated
interaction effects by showing stronger explanation effects in low
outcome favorability conditions (e.g., Colquitt & Chertkoff, 2002;
Folger & Martin, 1986; Gilliland, 1994; Gilliland & Beckstein,
1996; Ployhart et al., 1999; Schaubroeck et al., 1994). However,
the effects have been inconsistent within the studies; significance
is achieved for some outcomes but not for others.
Even though the studies cited previously remain somewhat
inconclusive, the fairness theory’s would counterfactual provides
support for the interaction prediction. Recall that the would counterfactual is used to create perceptions of negative impact (Folger
& Cropanzano, 1998, 2001). If this counterfactual is not activated,
then no injustice can be perceived. In such cases, any knowledge
gained from the explanation—whether it concerns feasibility or
justifiability—would be of limited importance. In contrast, when
individuals can easily envision better states of well-being, they
should attend more to the information provided in the explanation,
making explanation more powerful. Because studies that use low
outcome favorability should be more likely to activate the would
counterfactual, we made the following prediction:
Hypothesis 4: Explanations will have more beneficial effects
when outcome favorability is low than when outcome favorability varies.
Context
The studies on explanations have occurred in a variety of
contexts. The Appendix provides information on what exactly is
being explained within each of the studies included in our review.
In some cases, the outcome being explained has been a selection
decision or layoff decision (e.g., Brockner et al., 1990; Gilliland,
1994; Gilliland et al., 2001; Wanberg et al., 1999). Other studies
have examined situations where bosses deny resource requests or
take credit for subordinates’ ideas (e.g., Bies & Shapiro, 1987;
Bobocel, Agar, Meyer, & Irving, 1998). Still others have studied
explanations in the context of a pay cut, a merger, a change in job
structure, or a relocation (Daly & Geyer, 1994; Greenberg, 1990;
Rousseau & Tijoriwala, 1999; Schweiger & DeNisi, 1991).
These widely varying contexts likely have implications for the
would counterfactual in fairness theory. Specifically, it is likely
that the perceived impact of decision events is higher in some
contexts than in others, thereby increasing the importance of
explanation provision and adequacy. If so, the key question becomes how to differentiate contexts according to their perceived
impact. Unfortunately, there is no agreed upon framework or
taxonomy for classifying contexts according to their impact. There
are, however, frameworks that help explain why fairness matters in
various situations. These frameworks include the instrumental
model, the relational model, and the moral virtue model (for
reviews, see Cropanzano, Byrne, Bobocel, & Rupp, 2001; Cropanzano, Rupp, Mohler, & Schminke, 2001).
THE EFFECTS OF EXPLANATIONS
The instrumental model argues that fair treatment matters to
individuals because it conveys a sense of control over long-term
economic outcomes (Lind & Tyler, 1988; Thibaut & Walker,
1975). According to this model, the provision of an adequate
explanation should be more critical when the outcome has important economic consequences. As shown in the Appendix, several
of the studies in the literature have indeed occurred in contexts that
should possess instrumental impact. These include decisions that
directly affect the hiring, compensation, and termination of the
study respondents, as well as contexts involving resource requests
and budget proposals. All of these contexts have direct economic
consequences for the individual. Other studies have occurred in
contexts that lack direct economic consequences, such as when
coworkers are laid off, an individual receives negative criticism, or
a smoking ban is initiated.
The relational model argues that individuals value fairness,
because it signals that they are valued members of their key
relationships (Lind, 1995; Lind & Tyler, 1988; Tyler, 1999; Tyler
& Lind, 1992). Proponents of the relational model have consistently argued that fairness matters more when individuals feel
more connected to, and included in, organizational relationships
(Lind, 1995, 2001; Tyler, 1994; Tyler, Degoey, & Smith, 1996).
As Tyler (1999) summarized, “the strength of the social connection that people have to an organization—shapes the importance
they place upon the quality of treatment they receive from organizational authorities” (p. 230). Elsewhere he notes, “The relational model also predicts that people will care more about how
they are treated by others when they are dealing with people with
whom they share a common group membership” (p. 232). These
kinds of predictions have been supported in a number of studies
(e.g., Holbrook & Kulik, 2001; Smith, Tyler, Huo, Ortiz, & Lind,
1998).
As shown in the Appendix, several of the studies included in our
review have occurred in contexts that should possess relational
impact. That is, the studies have occurred in cases where the
participants shared a common group membership with the authority in question. These include studies in organizations that examine
pay cuts or freezes, disciplinary actions, important job changes, or
layoffs of coworkers. They also include laboratory studies that
create hypothetical group memberships through vignettes that discuss bosses taking credit for subordinates’ ideas, directors distributing funds inaccurately, and so on. Other studies have not occurred in contexts of shared group membership. For example,
studies that examine hiring decisions possess little relational impact, because the person receiving the explanation is not an organizational member. Likewise, studies that survey respondents who
have been laid off (often by approaching them during applications
for unemployment benefits) occur in settings where shared membership no longer exists. Thus, these studies possess less relational
impact, despite their obviously strong instrumental impact.
Finally, the moral virtue model argues that fairness is valued
simply because it is the right thing to do (Cropanzano et al., 2001;
Folger, 2001). In other words, it has value beyond its instrumental
or relational implications. According to this model, an adequate
explanation should be more critical when the outcome has violated
some moral standard. Several of the studies in the Appendix
occurred in contexts that could be termed morally charged. These
include cases where a boss takes credit for a subordinate’s idea,
disrespectful criticism is given, drug testing is initiated, deception
449
occurs, a diversity initiative decides hiring, or an employee is
sexually harassed. In these studies, explanations may or may not
be needed to quell instrumental concerns, but they will be needed
to deal with concerns about right and wrong.
To summarize, the instrumental, relational, and moral virtue
models can provide a theoretically grounded means of classifying
the impact of various contexts. To return to the language of
fairness theory, outcome favorability affects the level of impact, as
captured by the would counterfactual. In contrast, we would argue
that the study context affects the type of impact. As contexts take
on more multifaceted forms of impact, by possessing instrumental,
relational, and moral virtue implications, explanations should become more important. After all, the explanation is fulfilling multiple purposes in such cases. We therefore predicted:
Hypothesis 5: Explanations will have more beneficial effects
in contexts where the instrumental, relational, and moral
virtue impact are high rather than low.
Method
We conducted a meta-analytic review of the explanations literature to
test the hypotheses described previously. The methods for this review are
described in the following discussion.
Literature Search
First, we conducted a literature search of both the PsycINFO and Web
of Science databases for the years 1986 through 2001. We began our
review in 1986 to coincide with the publishing of Bies and Moag (1986),
which can be credited for increasing the visibility of explanations within
the justice literature. We later conducted a second literature search containing other databases (ABI-Inform and ERIC) and less restrictive years
(going back to 1968 when Scott and Lyman’s review was published). This
second search was conducted to ensure that our original criteria had not
omitted any relevant articles.
The key words used in our literature searches were explanations, accounts, excuses, justifications, and interactional justice. We also conducted
reference checks of several review articles in the explanations and justice
literatures. One such review was Colquitt, Conlon, Wesson, Porter, and
Ng’s (2001) meta-analysis of the organizational justice literature. That
review examined the effects of informational justice (an aggregate of
explanation provision and explanation adequacy) on a variety of outcomes,
although no moderators were examined in that review.
Our search yielded a total of 93 independent samples. Four of these were
redundant with other samples and were therefore excluded (Ball, Trevino,
& Sims, 1994; Bies & Shapiro, 1987, Study 3; Bies & Shapiro, 1988, Study
2; Daly, 1995). Twenty-seven samples were excluded because they did not
contain any of the relationships contained in our hypotheses (e.g., Bobocel
& Farrell, 1996; Crant & Bateman, 1993; Gaines, 1980; Giacalone &
Rosenfeld, 1984; Gonzales, 1992; Pence, Pendleton, Dobbins, & Sgro,
1982; Shapiro, Buttner, & Barry, 1994; Tedeschi, Riordan, Gaes, & Kane,
1983). For example, Crant and Bateman (1993), Gonzales (1992), and
Tedeschi et al. (1983) manipulated type of explanation but neither explanation provision nor explanation adequacy. Finally, eight samples were
excluded because they could not yield codeable information (e.g., Bies,
Martin, & Brockner, 1993; Greenberg, 1991). For example, Bies et al.
(1993) reported regression results, but meta-analysis requires zero-order
effect sizes (whether in the form of correlations, t or F statistics, or means
and standard deviations). These exclusions left us with a total of 54
independent samples, as shown in the Appendix.
SHAW, WILD, AND COLQUITT
450
Data Coding
All three authors were present for the coding of all information from
each article and had to reach consensus before any data point was entered.
For each study, we coded the following: the zero-order correlation for the
explanation relationship, the sample size, and the reliability of the explanation and dependent variables. We also coded information on the moderators examined in our hypotheses: the type of explanation used (justification vs. excuse); outcome favorability (varying vs. low); and the impact
of the context in terms of the instrumental, relational, and moral virtue
models (high vs. low).
whether the study relied on a laboratory or field design and whether
hypothetical vignettes were used. Neither methodological characteristic
affected the size of the explanation correlation. The average correlation for
the 25 laboratory studies was .31, as compared to .35 for the 29 field
studies (t ⫽ .78, ns). Similarly, the average correlation for the 14 vignette
studies was .34, as compared to .33 for the 40 nonvignette studies (t ⫽ .17,
ns). Thus, the choice of research methods seems to have little effect on
explanation correlations.
Results
Main Effects of Explanations
Meta-Analytic Methods
We followed the meta-analytic procedures described by Hunter and
Schmidt (1990). We first computed a weighted average correlation using
the study sample sizes. We then corrected these weighted average correlations for unreliability in both the independent and dependent variables. In
instances where reliability estimates were not reported, we used the
weighted average of all the reported reliabilities for that variable, which
ranged from .84 to .88. Reliability data were reported most frequently for
the five outcomes and for explanation adequacy, with the number of studies
failing to report reliability ranging from 2 to 5. Reliability was reported less
frequently for explanation provision, where 20 of the 30 studies manipulated provision. Such studies do not typically quantify the degree of
random error in the manipulations.
The meta-analysis results reported in our tables include both the uncorrected (r) and corrected (rc) correlation estimates. We also reported the
95% confidence interval for the uncorrected correlation. To provide an
index of the variation in the corrected correlations across studies, we
reported the standard deviation of the corrected correlation (SDrc). To
assess the extent to which sampling error and unreliability are driving that
variation, we calculated the percentage of variance explained by artifacts
(abbreviated Vart). According to Hunter and Schmidt (1990), moderators
likely exist in a relationship if artifacts fail to account for at least 75% of
the variance in the correlations. However, this 75% rule has been amended
by Mathieu and Zajac (1990) to be 60% in situations where there is no
correction for range restriction.
To test for moderators of explanation effects, we created a data set
composed of all the correlations from all the studies, along with the
relevant study characteristics. This data set was initially composed of 129
correlations (54 independent samples with an average of 2.39 correlations
per sample). We transformed the correlations so that higher values indicated more beneficial effects, regardless of the dependent variable. We
then aggregated that data set to the sample level by averaging across
multiple correlations. The end result was a data set with 54 aggregate
correlations and several dummy variables that were used to capture the
moderator variables. It is important to note that this aggregate data set was
only used to test our moderator hypotheses.
We should also note that methodological characteristics of the studies
were coded for potential use as control variables. Specifically, we coded
Hypothesis 1 predicted that explanation provision would have
beneficial effects on procedural and distributive justice as well as
cooperation, retaliation, and withdrawal responses. Hypothesis 2
made the same predictions for explanation adequacy. As shown in
Tables 1 and 2, both hypotheses received support. Explanation
provision was significantly related to procedural justice (r ⫽ .28,
rc ⫽ .32), distributive justice (r ⫽ .21, rc ⫽ .26), cooperation (r ⫽
.28, rc ⫽ .32), and retaliation (r ⫽ ⫺.33, rc ⫽ ⫺.39), although not
to withdrawal (r ⫽ ⫺.10, rc ⫽ ⫺.12). Explanation adequacy was
significantly related to all five outcomes (r ⫽ .49, rc ⫽ .54, for
procedural justice; r ⫽ .40, rc ⫽ .45, for distributive justice; r ⫽
.20, rc ⫽ .24, for cooperation; r ⫽ ⫺.37, rc ⫽ ⫺.43, for retaliation;
and r ⫽ ⫺.16, rc ⫽ ⫺.19, for withdrawal).
Table 2 also breaks down the response variables into more
specific categories. For the most part, differentiation between
in-role versus extra-role cooperation (e.g., task performance vs.
citizenship behaviors) did not alter the size of explanation effects,
neither did differentiation between active versus passive retaliation
(e.g., theft vs. blame). However, explanation adequacy did have a
significantly stronger effect on withdrawal from potential relationships (r ⫽ ⫺.59, rc ⫽ ⫺.69) than existing relationships (r ⫽ ⫺.10,
rc ⫽ ⫺.12), although the former is only based on k ⫽ 2.
Moderators of Explanation Effects
The results in Tables 1 and 2 suggested the existence of moderators. The variance accounted for by artifacts illustrated that
sampling error and unreliability only accounted for an average of
25% of the variance in the meta-analytic correlations. This falls far
below the 60% cutoff for the existence of moderators. We therefore examined the effects of our three moderators—type of explanation, outcome favorability, and context— on the size of the
explanation correlations. Table 3 presents correlations between the
moderator variables and the size of the explanation correlation
Table 1
Relationships Between Explanations and Justice Perceptions
Explanation provision
Explanation adequacy
Dependent
variable
r
95% CI
rc
SDrc
k
N
Vart (%)
r
95% CI
rc
SDrc
k
N
Vart (%)
Procedural justice
Distributive justice
.28
.21
.20, .35
.10, .32
.32
.26
.22
.29
24
18
4276
2414
13.16
12.03
.49
.40
.38, .60
.26, .54
.54
.45
.21
.27
12
10
3247
1985
5.44
7.04
Note. r ⫽ uncorrected weighted average correlation; 95% CI ⫽ 95% confidence interval around the uncorrected correlation; rc ⫽ corrected weighted
average correlation; SDrc ⫽ standard deviation of the corrected correlation; k ⫽ number of samples; N ⫽ total sample size; Vart ⫽ percentage of variance
in rc explained by study artifacts.
Note. Dash in table indicates that data were not obtained or reported. r ⫽ uncorrected weighted average correlation; 95% CI ⫽ 95% confidence interval around the uncorrected correlation; rc ⫽
corrected weighted average correlation; SDrc ⫽ standard deviation of the corrected correlation; k ⫽ number of samples; N ⫽ total sample size; Vart ⫽ percentage of variance in rc explained by study
artifacts.
15.63
14.18
22.02
36.49
56.37
—
8.94
37.80
6.29
3828
2050
1778
553
361
192
1829
1602
227
13
6
7
5
4
1
7
5
2
.16
.15
.15
.17
.15
.00
.23
.10
.28
.24
.18
.30
⫺.43
⫺.34
⫺.58
⫺.19
⫺.12
⫺.69
22
10
11
11
8
3
9
5
4
.28
.24
.28
⫺.33
⫺.32
⫺.35
⫺.10
⫺.07
⫺.14
Cooperation responses
In-role
Extra-role
Retaliation responses
Active
Passive
Withdrawal responses
Existing relationship
Potential relationship
.21, .35
.13, .35
.19, .36
⫺.44, ⫺.22
⫺.46, ⫺.18
⫺.44, ⫺.26
⫺.20, .01
⫺.16, .02
⫺.35, .08
.32
.28
.31
⫺.39
⫺.39
⫺.42
⫺.12
⫺.09
⫺.17
.20
.21
.17
.21
.23
.07
.20
.12
.27
3479
1263
2045
2084
1717
367
1294
763
531
17.33
21.24
21.18
12.26
9.21
100.00
26.41
62.01
15.92
.20
.16
.26
⫺.37
⫺.30
⫺.49
⫺.16
⫺.10
⫺.59
.13, .28
.05, .27
.16, .35
⫺.49, ⫺.25
⫺.43, ⫺.18
⫺.60, ⫺.38
⫺.31, ⫺.01
⫺.18, ⫺.02
⫺.93, ⫺.25
SDrc
rc
95% CI
k
SDrc
rc
95% CI
r
Dependent variable
Explanation provision
Table 2
Relationships Between Explanations and Response Variables
N
Vart (%)
r
Explanation adequacy
k
N
Vart (%)
THE EFFECTS OF EXPLANATIONS
451
taken from the 54 independent samples. The first column of the
correlation matrix shows that explanation correlations were lower
for justifications than for excuses (r ⫽ ⫺.39) and when outcome
favorability was varying rather than low (r ⫽ ⫺.31). Explanation
correlations were higher when the context possessed relational
impact (r ⫽ .30) and moral virtue impact (r ⫽ .41).
These results offer some support for Hypotheses 3–5. However,
it is important to note that there was moderate multicollinearity
among the moderator variables (see Table 3). We therefore tested
the moderator predictions using multiple regression, with the results shown in Table 4. It is important to note that the regression
was based on 36 samples rather than 54, because 18 samples used
self-report measures of explanations that were not classifiable as
justifications or excuses. The unstandardized Bs in Table 4 can be
interpreted as the mean difference in correlations explainable by
the variable in question. Thus, Table 4 shows that explanation
correlations were .14 lower for justifications than for excuses and
were .09 lower for studies with varying outcome favorability rather
than low ( p ⬍ .10). Table 4 also shows that the correlations were
.14, .11, and .23 higher when the study context possessed instrumental, relational, and moral virtue forms of impact, respectively.
Discussion
In general, the results of our meta-analytic review demonstrate
the predictive utility of fairness theory in the context of explanations. The mechanisms discussed in the theory were used to derive
main effect predictions for explanation provision and adequacy,
and both predictions received support. The specific counterfactuals
described by the theory were able to predict the differential effects
of excuses and justifications as well as the moderating effects of
outcome favorability and study context. These results suggest that
fairness theory, which had previously been used in only two
studies (Colquitt & Chertkoff, 2002; Gilliland et al., 2001), has
some utility as an integrative theory of reactions to decision events.
We are aware of no other theory that could have provided grounding for all five of the hypotheses tested in our review.
Overall Effects of Explanations
In terms of our specific results, we predicted that explanation
provision and adequacy would have beneficial effects on five
specific outcomes: procedural and distributive justice and cooperation, retaliation, and withdrawal responses. Our results show that
explanations were quite powerful, whether the dependent variable
was a justice judgment or a more general response. Indeed, many
of our results illustrate the practical significance of explanations.
For example, if we convert our explanation adequacy-retaliation
result into a binomial effect size display (see Rosenthal & Rosnow,
1991, pp. 281–286), we see that employees are 43% less likely to
retaliate after a decision if an adequate explanation is provided.
One interesting, though unexpected, pattern was that explanation adequacy tended to have more beneficial justice effects than
explanation provision. This suggests that an inadequate explanation may be deemed more unfair than failure to provide one at all.
A better understanding of adequacy is needed to understand such
a result. Although many studies assess adequacy only in vague
terms, some have operationalized adequacy in terms of length,
clarity, or legitimacy (e.g., Greenberg, 1994; Mansour-Cole &
SHAW, WILD, AND COLQUITT
452
Table 3
Correlations Between Size of Explanation Correlations and Moderator Variables
Variable
1.
2.
3.
4.
5.
6.
Size of explanation correlation
Justification (vs. excuse)
Varying (vs. low) outcome favorability
High (vs. low) instrumental impact
High (vs. low) relational impact
High (vs. low) moral virtue impact
M
SD
k
1
2
3
4
5
6
.33
.65
.39
.63
.57
.19
.19
.43
.49
.49
.50
.39
54
36
54
54
54
54
—
⫺.39*
⫺.31*
.03
.30*
.41*
—
.39*
.36*
⫺.25
⫺.36*
—
⫺.02
⫺.16
⫺.19
—
⫺.27*
⫺.33*
—
.22
—
Note. Dummy coding is as follows: justification ⫽ 1 vs. excuse ⫽ 0; varying outcome favorability ⫽ 1 vs. low ⫽ 0; high instrumental impact ⫽ 1 vs.
low ⫽ 0; high relational impact ⫽ 1 vs. low ⫽ 0; high moral virtue impact ⫽ 1 vs. low ⫽ 0.
* p ⬍ .05.
This result may seem surprising to those who view excuses as
somehow more self-serving and less forthright than justifications.
However, the key is to realize that excuse has become a pejorative
term in everyday language. As used in the academic literature, it
means only that the content of the explanation focuses on a denial
of responsibility or claim of mitigating circumstances. We suspect
that framing our predictions in terms like causal accounts or
mitigating accounts would make our results seem less surprising.
In any event, future research should continue to compare excuses and justifications to assess where and when they function
best. Such research would benefit from a more detailed process
focus that explores the specific cognitions that are triggered when
any explanation is received. For example, justifications may incite
more cognitive resistance than excuses, because it may be easier to
disagree with the merits of a superordinate goal than the reality of
a mitigating circumstance. Alternatively, individuals may be biased against accepting that a negative outcome was justifiable due
to self-serving or egocentric biases.
A potentially useful tool for process-focused explanations research is verbal protocol analysis (Ericsson & Simon, 1980, Ericsson & Simon, 1993), where individuals are asked to “think out
loud” in response to some stimulus. This kind of analysis could
uncover the different cognitive processes that are prompted by
excuses and justifications. Of course, the issue of adequacy again
becomes critical. It may be that an illegitimate or insincere excuse
is somehow more damaging than an illegitimate or insincere
justification. After all, at least the latter admits some responsibility
for the event, even if the specific reasons given are not convincing.
Scott, 1998; Mellor, 1992). Similarly, Shapiro et al. (1994) showed
reasonableness to be a key dimension of adequacy.
It would seem that a cursory, unreasonable, or illegitimate
explanation could be harmful in two respects. First, the benefits to
the could or should counterfactuals in fairness theory would fail to
materialize. Second, the unreasonable or illegitimate content might
itself violate ethical standards, which would further amplify the
should component of the theory. This second effect would not
occur to the same extent in the absence of any explanation. To test
such notions, future researchers should consider adequacy and
provision together, so that inadequate explanations can be compared to no explanation conditions. Such research would benefit
from tapping multiple dimensions of adequacy, including legitimacy, reasonableness, or sincerity.
Boundary Conditions for Explanation Effects
Even though the overall effects of explanations are impressive,
they clearly vary across studies. In an attempt to explain these
inconsistencies, we used fairness theory to identify moderator
variables that could alter the strength of explanation effects. One
such moderator was type of explanation. We reasoned that excuses
are capable of deactivating both the could and should components
of fairness theory. An adequate excuse shows that no other actions
were feasible, which deactivates the could counterfactual. Moreover, because comparisons to ethical standards only occur for the
feasible set, the should counterfactual can also be deactivated. Our
results support this reasoning, as excuses had more beneficial
effects than justifications.
Table 4
Size of Explanation Correlations as a Function of Moderator Variables
Regression step
B
␤
⌬R2
R2
1. Justification (vs. excuse)
Varying (vs. low) outcome favorability
2. High (vs. low) instrumental impact
High (vs. low) relational impact
High (vs. low) moral virtue impact
⫺.14†
⫺.09
.14†
.11†
.23†
⫺.30†
⫺.23
.35†
.28†
.49†
.20*
.20*
.30*
.50*
Note. k ⫽ 36 samples after listwise deletion of missing data. Dummy coding is as follows: justification ⫽ 1
vs. excuse ⫽ 0; varying outcome favorability ⫽ 1 vs. low ⫽ 0; high instrumental impact ⫽ 1 vs. low ⫽ 0; high
relational impact ⫽ 1 vs. low ⫽ 0; high moral virtue impact ⫽ 1 vs. low ⫽ 0.
* p ⬍ .05, two-tailed. † p ⬍ .05, one-tailed.
THE EFFECTS OF EXPLANATIONS
In addition to explanation type, we examined the moderating
role of outcome favorability. We predicted that explanations would
be more beneficial in studies with unfavorable outcomes. After all,
fairness theory argues that the activation of the would counterfactual is necessary for individuals to attend to could and should
issues. Our results tend to support this reasoning, as explanations
had stronger effects in studies using low outcome favorability.
These results also offer additional support for the more general
outcome Favorability ⫻ Process Fairness interaction seen in the
larger organizational justice literature (Brockner & Wiesenfeld,
1996). It may be that individuals simply attend less to the explanation content in the absence of unfavorable outcomes, or it may
be that the explanation actually creates conflicting reactions. The
gesture of the explanation may be appreciated, but the content of
it could cause individuals to react to the event less favorably. This
would be especially likely if the positive outcome was found to be
a function of the context, rather than the individuals’ merit.
Finally, we also reasoned that explanations would become particularly critical in certain study contexts. The instrumental, relational, and moral virtue models were used to create a framework
for gauging the impact of various contexts. Our results show that
all three models can shed light on which contexts make explanations most impactful. Explanation effects were particularly strong
when the context had instrumental (i.e., economic) implications, as
in hiring, layoff, compensation, budget, and resource decisions.
However, our results also suggest that explanations have value
beyond their ability to shed light on future economic well-being.
Specifically, explanations are more powerful when the recipient
possesses high levels of inclusion or shared group membership.
They are also more powerful when the explanation concerns a
morally charged event, such as deception or controversial organizational decisions (e.g., drug testing, diversity initiatives). Thus,
explanations can be important even for decisions with no economic consequences, because they can reaffirm the quality of a
relationship or counteract a presumed ethical violation. Of course,
process-focused research is needed to identify exactly what cognitions explanations trigger in different contexts. It may be that
different types of explanations become more appropriate in different kinds of contexts. For example, individuals may be more
resistant to justifications in morally charged contexts.
Practical Implications
Taken together, our results illustrate that the failure to give an
explanation— or the use of an inadequate one— can lead to negative employee reactions. Many of these employee reactions, most
notably retaliation and withdrawal, carry with them substantial
costs to the employer. With that in mind, we return to the notion
that tough times can lead to bad management (Folger & Skarlicki,
2001). Recall that Folger and Skarlicki (2001) argued that the
delivery of bad news causes managers to distance themselves
because of emotional distress, fear of being blamed, or concerns
about making the situation worse. Managers may therefore believe
that explaining a decision will only worsen the situation (as in the
opening quote from The Rivals). They may also be worried about
triggering a lawsuit based on some detail, which leads to an
explanation that really explains nothing at all (as in the opening
quote from Don Juan).
453
If tough times really do lead to these forms of bad management,
then there is no better time to emphasize the importance of explanations. Newspaper headlines announce layoffs and pay cuts on an
almost daily basis. For example, a recent front page article in USA
Today cited a Towers Perrin survey showing that 53% of companies were likely to cut pay to reduce costs versus 37% before
September 11th (Armour, 2002). The article went on to point out
that explanations for the pay cuts have “never been more important,” otherwise employees may attribute their cut to a decrement
in job performance (p. A1).
Our results support the prescriptions made in that article. Managerial distancing will likely take a bad situation and make it
worse, as employees react to the unexplained bad news with less
cooperation, more retaliation, and more withdrawal. Similarly,
worries about lawsuits may ironically cost more money than they
save, given that litigation is a form of retaliation that should be
decreased—not increased— by adequate explanations. Dunford
and Devine (1998) made a similar argument in the context of
wrongful termination, arguing that adequate explanations signify
fair treatment and therefore lower the likelihood of legal action.
In summary, we would echo earlier narrative reviews that argued that explanations should become a necessary ingredient in
virtually any decision-making process (e.g., Bies & Moag, 1986;
Folger & Bies, 1989; Tyler & Bies, 1990). In particular, explanations are vital in contexts with instrumental, relational, and moral
virtue implications and when outcomes are unfavorable. Indeed,
when one considers the practical benefits of explanations (e.g., a
43% reduced likelihood of retaliation) relative to their costs, it is
difficult to envision an adequate excuse (or justification) for not
offering them.
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THE EFFECTS OF EXPLANATIONS
457
Appendix
Summary of Study Characteristics
Impact of context
Author string
N
Outcome being explained
1. Folger & Martin (1986)
160
2. Bies (1987a)a
3. Bies & Shapiro (1987; Study 1)
102
56
4. Bies & Shapiro (1987; Study 2)
87
5. Bies & Shapiro (1988; Study 1)
96
Granting (or not granting)
opportunity to participate in
second experiment
Boss denying a resource request
Hypothetical boss taking credit
for subordinate’s idea
Reception of a smaller than
expected hypothetical budget
Failure to grant second
interview
Boss denying a resource
request
Hypothetical director not
distributing funds using
correct criteria
Layoffs of coworkers
Receiving negative criticism
6. Bies et al. (1988)
121
7. Giacalone (1988)
135
8. Brockner et al. (1990)
9. Baron (1990; Study 1)
597
60
10. Greenberg (1990)
11. Konovsky & Cropanzano
(1991)
12. Konovsky & Folger (1991)
13. Schweiger & DeNisi (1991)
353
147
14. Shapiro (1991)
192
15. Mellor (1992)
16. Ball et al. (1993)c
17. Greenberg (1993)
335
79
102
18. Rousseau & Aquino (1993)
121
19. Brockner et al. (1994; Study 1)
20. Brockner et al. (1994; Study 2)
21. Daly & Geyer (1994)d
218
150
171
22. Gilliland (1994)
270
23. Greenberg (1994)
732
24. Schaubroeck et al. (1994)
165
25. Martin et al. (1995)
26. Conlon & Murray (1996)
147
121
27. Gilliland & Beckstein (1996)
106
28. Cobb et al. (1997)
430
29. Conlon & Ross (1997)
234
30. Bobocel et al. (1998)
135
31. Davidson & Friedman (1998;
Study 1)
32. Davidson & Friedman (1998;
Study 2)
33. Davidson & Friedman (1998;
Study 4)
113
195
19
97
241
Receiving a temporary pay cut
The institution of a drugtesting policy
Layoff of study respondents
Merger with another Fortune
500 firm
Being deceived (or not
deceived) by a fellow
participant, costing $10
Layoff of coworkers
Receiving a disciplinary action
Receiving the same (or less)
compensation for
experiment than was
promised
Hypothetical layoffs of study
respondents
Layoff of study respondents
Layoff of coworkers
Decision to relocate work site
for either expansion or
consolidation
Being hired (or not hired) for
a job
Institution of a smoking ban
Receiving a temporary pay
freeze
Layoff of study respondents
Purchased product found to be
unsatisfactory
Getting a journal submission
accepted or rejected
Receiving an annual performance evaluation
Imposition of a 3rd party
negotiation settlement
Boss denying a resource
request
Hypothetical boss taking credit
for subordinate’s idea
Hypothetical boss taking credit
for subordinate’s idea
Hypothetical boss taking credit
for subordinate’s idea
Explanation
type
Outcome
favorability
Instrumental
Relational
Moral virtue
Excuse
Varies
(manip)
Low
Low
Low
Varies
Excuse
Low
Low
High
Low
High
High
Low
High
Justification
Low
High
High
Low
Justification
Low
High
Low
Low
Excuse
Low
High
High
Low
Varies
(manip)
Low
High
High
Low
Varies
Varies
(manip)
Justification
Varies
Low
Low
Low
Low
High
High
Low
High
Low
Low
High
Low
High
High
Low
High
Excuseb
Varies
Low
Varies
High
High
Low
High
Low
Low
Varies
(manip)
Varies
(manip)
High
High
High
Excuse
Varies
Justification
Low
Low
Varies
(manip)
Low
High
High
High
High
Low
Low
Low
High
Justification
Low
High
Low
Low
Varies
Varies
Varies
Low
Low
Varies
High
Low
Low
Low
High
High
Low
Low
Low
Justification
High
Low
Low
Low
High
Low
Justification
Varies
(manip)
Varies
(manip)
Low
High
High
Low
Varies
Varies
Low
Low
High
High
Low
Low
Low
Low
Justification
Varies
High
High
Low
Varies
Varies
High
High
Low
Varies
(manip)
Justification
Low
High
High
Low
Low
High
High
Low
Excuse
Low
Low
High
High
Excuse
Low
Low
High
High
Excuse
Low
Low
High
High
Justification
(Appendix continues)
SHAW, WILD, AND COLQUITT
458
Appendix (continued)
Impact of context
Author string
34. Mansour-Cole & Scott (1998)
35. Miles & Klein (1998)
36. Richard & Kirby (1998)
N
Outcome being explained
217
48
Layoff of coworkers
Being excused (or not
excused) from participating
in second phase of study
Being hired for a hypothetical
job based on a diversity
initiative rather than merit
Hypothetical layoff of study
respondents
Boss denying an unspecified
request
Receiving a benefits package
The pay and benefits received
at work
Being hired (or not hired) for
a hypothetical job
Being admitted (or not
admitted) to graduate school
Implementation of team-based
structure
Layoff of study respondents
Being hired (or not hired) for
a hypothetical job
Being sexually harassed
90
37. Skarlicki et al. (1998)
123
38. Tata (1998)
186
39. Tremblay et al. (1998)
40. Jones et al. (1999)
285
612
41. Ployhart et al. (1999; Study 1)
156
42. Ployhart et al. (1999; Study 2)
35
43. Rousseau & Tijoriwala (1999)
501
44. Wanberg et al. (1999)
45. Horvath et al. (2000)
108
186
46. Tata (2000)
351
47. Colquitt (2001; Study 1)
48. Colquitt (2001; Study 2)
301
337
49. Gilliland et al. (2001; Study 1)
120
50. Gilliland et al. (2001; Study 2)
51. Gilliland et al. (2001; Study 3)
32
380
52. Weaver & Conlon (in press)
110
53. Wiechmann & Ryan (2000;
Study 2)
54. Colquitt & Chertkoff (2002)
201
168
Receiving a grade in a course
The pay and promotions
received at work
Not being hired for a
hypothetical job
Not being hired for a job
Not being hired for a
hypothetical job
Receiving (or not receiving)
preferred task in a lab study
Being hired (or not hired) for
a hypothetical job
Participants receiving (or not
receiving) their requested
group member
Explanation
type
Outcome
favorability
Justification
Justification
Instrumental
Relational
Moral virtue
Low
Varies
(manip)
Low
Low
High
Low
Low
Low
Justification
Low
High
Low
High
Varies
Low
High
Low
Low
Varies
Low
Low
High
Low
Varies
Varies
Varies
Varies
High
High
High
High
Low
Low
Justification
Varies
(manip)
Varies
(manip)
Varies
High
Low
Low
High
Low
Low
Low
High
Low
Low
Varies
(manip)
Low
High
High
Low
Low
Low
Low
Low
High
High
Varies
Varies
Low
High
Low
High
Low
Low
Justification
Low
High
Low
Low
Excuse
Varies
(manip)
Varies
(manip)
Justification
Low
Low
High
High
Low
Low
Low
Low
Varies
(manip)
Varies
(manip)
Varies
(manip)
Low
Low
Low
High
Low
Low
Low
Low
Low
Justification
Justification
Varies
Justification
Varies
(manip)
Varies
Varies
Justification
Note. manip ⫽ varied by manipulating it across experimental conditions.
Uses same sample as Bies and Shapiro’s (1987) Study 3 and Bies and Shapiro’s (1988) Study 2. b As reflected in the authors’ causal accounts variable.
Referential account results were not coded. c Uses same sample as Ball et al. (1994). d Uses same sample as Daly (1995).
a
Received February 20, 2002
Revision received May 6, 2002
Accepted May 20, 2002 䡲
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To Justify or Excuse?: A Meta-Analytic Review University of Florida