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A Meta-Analytic Examination of the Goal Orientation Nomological Net
Stephanie C. Payne and Satoris S. Youngcourt
Texas A&M University
J. Matthew Beaubien
Aptima, Inc.
The authors present an empirical review of the literature concerning trait and state goal orientation (GO).
Three dimensions of GO were examined: learning, prove performance, and avoid performance along with
presumed antecedents and proximal and distal consequences of these dimensions. Antecedent variables
included cognitive ability, implicit theory of intelligence, need for achievement, self-esteem, general
self-efficacy, and the Big Five personality characteristics. Proximal consequences included state GO,
task-specific self-efficacy, self-set goal level, learning strategies, feedback seeking, and state anxiety.
Distal consequences included learning, academic performance, task performance, and job performance.
Generally speaking, learning GO was positively correlated, avoid performance GO was negatively
correlated, and prove performance GO was uncorrelated with these variables. Consistent with theory,
state GO tended to have stronger relationships with the distal consequences than did trait GO. Finally,
using a meta-correlation matrix, the authors found that trait GO predicted job performance above and
beyond cognitive ability and personality. These results demonstrate the value of GO to organizational
researchers.
Keywords: trait goal orientation, state goal orientation, meta-analysis, self-regulation, job performance
Recent years have witnessed a substantial body of research
concerning relationships among traitlike motivational characteris-
tics and measures of performance. One variable that has received
a great deal of attention in organizational research is goal orien-
tation (GO). GO refers to one’s dispositional or situational goal
preferences in achievement situations. Originating in the educa-
tional psychology literature, organizational psychology research-
ers have proposed that GO plays an important role in a variety of
human resources decisions, such as recruitment (e.g., Rynes &
Gerhart, 1990), selection (e.g., L. Roberson & Alsua, 2002), train-
ing (e.g., K. G. Brown, 2001), and performance appraisal (e.g.,
VandeWalle & Cummings, 1997). GO also appears to play an
important role in other work-related topics such as organizational
climate and culture (e.g., Potosky & Ramakrishna, 2002), organi-
zational change (e.g., Gully & Phillips, 2005), leadership (e.g.,
Janssen & Van Yperen, 2004), and team building (e.g., Bunderson
& Sutcliffe, 2003).
As organizational researchers have incorporated GO into their
work, several questions have emerged (DeShon & Gillespie,
2005). The purpose of this study was to answer the following
questions: What is the stability of trait GO over time? To what
extent are the dimensions of trait and state GO interrelated? How
similar is GO to other individual differences? How well do GO
dimensions predict various self-regulatory constructs? Does GO
predict job performance above and beyond well-established pre-
dictors? We addressed these questions by meta-analyzing the
relationships among GO dimensions and key variables of interest.
By answering these questions, we believe this quantitative review
provides the groundwork for future theoretical advancement.
GO
History
The concept of GO was independently conceived by educational
psychologists during the 1970s and 1980s. In an attempt to apply
Atkinson’s (1964) theory of achievement motivation to the class-
room, Eison (1979) described students as possessing either learn-
ing or grade orientations. According to Eison, a learning orienta-
tion was the predominant attitude held by students who
approached college as an opportunity to acquire knowledge and
obtain personal and educational enlightenment. Conversely, a
grade orientation was the predominant attitude held by students
who viewed obtaining a high course grade as an end in itself
(Eison, 1979). Eison (1979) developed the Learning Orientation–
Grade Orientation Scale to assess these orientations. These two
orientations were originally conceptualized as opposite ends of an
underlying continuum. However, Eison later reconceptualized
them as being independent and revised his measure accordingly
(Learning Orientation–Grade Orientation Scale II; Eison, Pollio, &
Milton, 1982).
Around the same time, Nicholls (1975, 1976, 1978) was study-
ing achievement motivation to identify the conditions under which
grade school children would set excessively high or low task-
Stephanie C. Payne and Satoris S. Youngcourt, Department of Psychol-
ogy, Texas A&M University; J. Matthew Beaubien, Aptima, Inc., Woburn,
Massachusetts.
A previous version of this article was presented at the 14th Annual
Society for Industrial and Organizational Psychology Conference, Atlanta,
Georgia, April 1999. We thank Winfred Arthur Jr., Gilad Chen, Jose
Cortina, Sandy Fisher, Stan Gully, and Joe Martocchio for their comments
on previous versions of this article. We also thank Rachel Dunlap, Renee
Hall, Amanda Halverson, Leigh Henderson, Shannon Lemon, and Cecilia
Prause for their administrative assistance.
Correspondence concerning this article should be addressed to
Stephanie C. Payne, Department of Psychology, Texas A&M University,
4235 TAMU, College Station, TX 77843-4235. E-mail: scp@psyc
.tamu.edu
Journal of Applied Psychology Copyright 2007 by the American Psychological Association
2007, Vol. 92, No. 1, 128–150 0021-9010/07/$12.00 DOI: 10.1037/0021-9010.92.1.128
128
related goals as well as developmental issues related to demon-
strating competence. According to Nicholls (1975), the most im-
portant factor was how individuals choose to define success. He
hypothesized there were two conceptions of success: task involve-
ment, in which individuals compare themselves with their past
performance (self-referent), or ego involvement, in which individ-
uals compare their performance with others (external referent).
Independent of Eison (1979) and Nicholls (1975), Dweck (1975,
1986, 1989) and her colleagues (Dweck & Elliott, 1983; Dweck &
Leggett, 1988) were researching achievement motivation from a
developmental perspective, focusing on how children develop and
demonstrate their ability in achievement settings. They noticed that
when several high-ability children encountered difficulty, they
would make negative comments about the task and/or their ability,
use maladaptive strategies, and eventually develop feelings of
helplessness. Taking a social– cognitive approach in which indi-
viduals use beliefs, values, and goals to define themselves
(Markus, 1977), Dweck (1986) postulated that children tend to
hold either learning or performance goals. Dweck (1986) proposed
individuals with learning goals approach a task with the goal of
learning for its own sake, whereas individuals with performance
goals attempt to gain favorable judgments or avoid negative judg-
ments from others. Further, Dweck hypothesized these goals were
based on one’s individual theory of intelligence (M. Bandura &
Dweck, 1985; Elliott & Dweck, 1988). Individuals who adopt an
incremental theory of intelligence are likely to believe intelligence
and performance can be improved through increased effort and
therefore adopt learning goals. On the other hand, individuals who
adopt an entity theory of intelligence are likely to believe intelli-
gence and performance are fixed and therefore adopt performance
goals. Because it is impossible for an individual to simultaneously
adopt both beliefs, the two goals and subsequent GOs were ini-
tially hypothesized to exist at opposite ends of an underlying
continuum (Dweck, 1986, 1989).
It is important to note that whereas there are some similarities
between Nicholls’s (1975) and Dweck’s (1986) conceptualizations
of GO, the underlying theory as to why individuals have such
orientations is quite different. Nicholls (1975) attributed GO to an
internal versus external referent, whereas Dweck (1986) attributed
it to an incremental versus entity theory of intelligence. Although
some support for each theory has been demonstrated in research
examining each theory independently, no research to our knowl-
edge has pitted one GO theory against the other in an effort to
determine which theory is better or more accurate. We attempt to
contrast the empirical evidence supporting each theory through the
variables we examined.
1
Over the years, other researchers also contributed to the early
theorizing about GO (cf. Ames, 1984; Maehr, 1983). Nearly a
decade after conception, GO was introduced into the organiza-
tional literature (Farr, Hoffman, & Ringenbach, 1993). Building on
Dweck’s (1986) early work, Farr et al. (1993) described GO as a
mental framework that determines how individuals interpret and
respond to achievement situations. They compared and contrasted
learning GO (LGO) and performance GO (PGO) on a number of
organizational issues, such as task interest, goal setting, feedback
seeking, and trainee motivation. They also provided practical
guidance for a number of organizational topics, such as training
and feedback.
Although there were some minor differences among these early
theorists, they were in near universal agreement that learning goals
were correlated with positive behaviors, such as setting realistic
goals and persisting in the face of failure. By comparison, perfor-
mance goals were associated with negative behaviors, such as
maladaptive performance strategies and feelings of helplessness.
On the basis of these early results, many researchers have come to
believe learning goals are always correlated with positive out-
comes, and performance goals are always correlated with negative
ones (Brophy, 2004; see Midgley, Kaplan, & Middleton, 2001, for
an exception). For some researchers, this belief has become so
widespread that it is no longer questioned. Therefore, one of our
goals was to test the veracity of this claim.
To our knowledge, only three other GO meta-analyses have
been conducted. In 1997, Utman meta-analyzed 24 studies that
manipulated participants’ GO prior to completing an experimental
task. He demonstrated that learning goals lead to better perfor-
mance than do performance goals, but this advantage appeared to
be limited to more complex tasks. Rawsthorne and Elliot (1999)
meta-analyzed 30 studies and found experimentally induced per-
formance goals undermined intrinsic motivation compared with
experimentally induced learning goals, particularly when perfor-
mance-avoidance goals were induced and combined with positive
competence-confirming feedback. Day, Yeo, and Radosevich
(2003) meta-analyzed 127 studies and found a three-factor model
bifurcating performance goals into separate prove and avoid di-
mensions (described later) explained 7% more variance in aca-
demic performance than a two-factor model.
Our meta-analysis differs from the previous meta-analyses in
five important ways. Using a much larger sample of 197 indepen-
dent samples from 157 studies conducted between 1979 and 2002,
we conducted meta-analytic examinations of (a) the temporal
stability of trait GO; (b) the relationships between and among the
dimensions of trait and state GO; (c) the relationships between trait
GO and 10 antecedents, 8 proximal outcomes, and 4 distal out-
comes; (d) the relationships between state GO and 4 distal out-
comes; and (e) the incremental validity of trait GO over and above
established predictors of job performance.
The Nature of the GO Construct
The psychological nature of the GO construct has been a topic
of debate. GO has been treated by organizational researchers as a
stable, traitlike, individual-difference characteristic (e.g., Colquitt
& Simmering, 1998); a situationally specific state yoked to the task
or context at hand (e.g., Stevens & Gist, 1997); or an experimen-
tally induced state (e.g., Steele-Johnson, Beauregard, Hoover, &
Schmidt, 2000). Initial theoretical formulations of GO described it
as dispositional (e.g., Nicholls, 1989), varying as a function of
one’s stable (Robins & Pals, 1998) implicit theory of intelligence
(Dweck, 1986, 1989). However, some of those same researchers
manipulated participants’ theories of intelligence in laboratory
settings (Dweck & Leggett, 1988). It seems logical GO could exist
as both a trait and a state, with trait GO having a direct effect on
state GO. Indeed, a number of psychological variables are believed
to operate accordingly (e.g., self-efficacy, self-esteem, anxiety).
1
We thank an anonymous reviewer for pointing this out.
129
GOAL ORIENTATION META-ANALYSIS
However, what is not clear is which conceptualization of GO is
most relevant and of most importance to organizational research-
ers. By examining both trait and state GO, we intended to dem-
onstrate from a predictive validity standpoint which operational-
ization of GO explains more variance in important outcomes.
Temporal Stability of Dispositional GO
Across organizational studies, GO is most often conceptualized
as a disposition and measured as a traitlike individual-difference
variable. However, the stability of dispositional GO over time has
yet to be determined. In initial efforts to examine its stability,
Button, Mathieu, and Zajac (1996) described GO as a “somewhat
stable individual difference factor that may be influenced by
situational characteristics” (p. 28). Personality researchers have
found other dispositional variables to be quite stable over time. For
example, mean coefficients of stability for the Big Five personality
variables ranged from .69 to .76 over a 1- to 2-year time period
(Viswesvaran & Ones, 2000). Thus, trait GO was expected to be
relatively stable over time as well.
The Dimensionality of GO and the Relationships Among
the Dimensions
As noted, initially GO researchers believed GO was a bipolar
construct. Thus, individuals could be high on one orientation or the
other but not simultaneously high (or low) on both. Over time,
researchers began to question this assumption and subsequently
developed separate scales for measuring LGO and PGO (Button et
al., 1996; Eison, Pollio, & Milton, 1986). This position was best
advocated by Button et al. (1996), who argued individuals often
have multiple, competing goals. Using the example of competitive
divers, they noted individuals often strive to outperform their
competition, while simultaneously improving on their own prior
performance. This finding suggests that individuals can have si-
multaneously high levels of LGO and PGO.
More recently, VandeWalle (1993, 1996, 1997) argued PGO is,
in fact, multidimensional. Noting PGO was originally defined as
the desire to gain favorable judgments and avoid unfavorable
judgments about one’s ability (Heyman & Dweck, 1992), Vande-
Walle (1996) argued for partitioning PGO into two dimensions:
prove and avoid. He defined prove performance GO (PPGO) as
“the desire to prove one’s competence and to gain favorable
judgments about it” and avoid performance GO (APGO) as “the
desire to avoid the disproving of one’s competence and to avoid
negative judgments about it.” He subsequently demonstrated a
three-factor model was superior to a two-factor model.
Elliot and his colleagues have forwarded similar arguments
proposing a trichotomous, approach–avoidance–achievement goal
framework (Elliot, 1994; Elliot & Harackiewicz, 1996). Building
on the classic approach to achievement motivation (Atkinson,
1957), Elliot (1994) distinguished between approach and avoid-
ance motivation by partitioning PGO into separate approach and
avoidance components. He described performance-approach goals
as focusing on the attainment of competence relative to others,
whereas performance-avoidance goals focus on avoiding the per-
ception of incompetence relative to others. Elliot and his col-
leagues have shown that approach and avoidance components have
different antecedents (Elliot & Church, 1997) and outcomes (Elliot
& Harackiewicz, 1996). In fact, whereas researchers have tended
to associate PGO with negative outcomes, when considering the
dimensionality of PGO, some researchers have noted it is really the
avoidance dimension that is dysfunctional (e.g., Brophy, 2004).
Because the approach–avoidance distinction appears both concep-
tually and empirically meaningful, several researchers (Conroy,
Elliot, & Hofer, 2003; Linnenbrink & Pintrich, 2000; Pintrich,
2000) have proposed LGO should also be divided into approach
and avoidance components in order to focus more on the influence
of these aspects. However, because the 2 ⫻2 framework is
relatively new and little empirical research has been conducted
using it, we examined the three-component conceptualization of
GO.
In addition to questions about the dimensionality of GO, the
relationships among the dimensions are not clear, as the various
GO theorists propose competing perspectives. Dweck’s (1986,
1989) initial work treated LGO and PGO as two ends of the same
continuum, suggesting the relationship between them is negative.
On the other hand, Nicholls (1984, 1989) proposed task and ego
GOs were orthogonal (unrelated). Elliot (1994) and VandeWalle
(1993) have argued that APGO is to some degree the opposite of
LGO, because the two orientations foster perceptions of errors and
difficult tasks quite differently, suggesting a negative relationship
between these dimensions. Most theorists expect the PGO dimen-
sions to be positively related to one another, perhaps because they
both contain an “other” referent (Elliot, 1994). Thus, identifying
the interrelationships among the dimensions is one way to compare
empirical support for the different GO theories.
We attempt to further define the nomological network for the
GO dimensions by exploring their relationships with a wide range
of variables we label as antecedents and proximal and distal
consequences. We use the framework depicted in Figure 1 to
organize the variables examined in this study. This framework is
based on A. Bandura’s (1989, 1991) social– cognitive theory of
self-regulation and previous GO research (e.g., Chen, Gully,
Whiteman, & Kilcullen, 2000; Phillips & Gully, 1997; Porath &
Bateman, 2006), in that self-regulatory constructs and processes
mediate the relationship between individual-difference variables
and various outcomes.
Antecedents of GO
In this section, we explore several variables believed to contrib-
ute to the development of GO. These variables include cognitive
ability, implicit theory of intelligence, need for achievement, the
Big Five personality characteristics, self-esteem, and general self-
efficacy.
Cognitive Ability
Eison and his colleagues (Eison, 1979, 1981) found that, in
comparison with grade-oriented students, learning-oriented stu-
dents possessed higher levels of cognitive ability. However,
Dweck and her colleagues found no such relationship (M. Bandura
& Dweck, 1985; Dweck, 1986). Over time, researchers have found
mixed results concerning the relationship between GO and cogni-
tive ability. However, a substantial body of theory and research
suggests motivational and ability traits are generally uncorrelated
130 PAYNE, YOUNGCOURT, AND BEAUBIEN
(Ackerman & Heggested, 1997; Kanfer, Dugdale, & McDonald,
1994; Kuhl & Fuhrmann, 1999).
Implicit Theory of Intelligence
Dweck (1986) theorized that implicit beliefs about the stability
of intelligence determine the types of goals people adopt. Individ-
uals who believe intelligence is malleable are more likely to adopt
learning goals, whereas individuals who believe intelligence is
fixed are more likely to adopt performance goals. Consistent with
Dweck’s (1986) theory, we expected measures of implicit theory
of ability to correlate strongly with the GO dimensions. Most
scales of implicit beliefs are bipolar, with higher numbers repre-
senting an entity theory of intelligence. Therefore, we expected
implicit theory of intelligence to relate positively to the PGO
dimensions and negatively to LGO.
Need for Achievement
GO dimensions have been conceptualized as concrete manifes-
tations of Atkinson’s (1957) competence-relevant motives: need
for achievement and need to avoid failure (fear of failure). Ac-
cordingly, Elliot and Church (1997) proposed that each of the GO
dimensions has a unique antecedent profile composed of achieve-
ment motivation, competence expectancies, and fear of failure.
Given the theoretical link to competence-relevant motives, we
expected need for achievement to relate positively to LGO and
PPGO and negatively to APGO.
Personality Characteristics
Years of research have led to the identification of five robust
personality traits frequently referred to as the “Big Five.” These
include agreeableness, conscientiousness, extraversion, emotional
stability, and openness to experience (Digman, 1990; Goldberg,
1990). Research documenting the underlying physiological com-
ponents, heritability, and stability of the Big Five implies they can
be considered source traits, which invoke an explanatory mecha-
nism that can be used for prediction (Eysenck, 1990; McCrae &
Costa, 1994).
GO appears to be a “compound” trait made up of various aspects
of the Big Five (Hough & Schneider, 1996). For example, Hough
(1992) argued that both extraversion and conscientiousness en-
compass aspects of achievement, which suggests both variables
will correlate positively with LGO and PPGO. Similarly, Elliot
and Church (1997) hypothesized that LGO and APGO are
grounded in achievement motivation, a component of conscien-
tiousness. Finally, emotional stability might also be expected to
correlate negatively with APGO as both describe anxiety-related
dispositional tendencies.
Self-Esteem
Self-esteem refers to an overall affective assessment of one’s
worth, value, or importance as an individual (Rosenberg, 1965). In
general, self-esteem is believed to be a generalized, yet relatively
enduring, traitlike characteristic. Despite the inclusion of self-
esteem in many studies, there does not appear to be a compelling
theoretical rationale for a direct relationship between self-esteem
and GO. Nevertheless, it is well-known that goal fulfillment con-
tributes to one’s self-esteem (e.g., Parrott & Hewitt, 1978). Thus,
the relationship between self-esteem and GO may depend on
whether corresponding goals are fulfilled. Therefore, the relation-
ship between self-esteem and GO may be moderated by goal
fulfillment.
General Self-Efficacy
General self-efficacy is a relatively enduring belief in one’s
capacity to perform across a wide range of situations and tasks
(Chen, Gully, & Eden, 2001). The relationship between GO and
general self-efficacy may vary as a function of their mutual rela-
tionships with the theory of intelligence. Kanfer (1990) suggested
Antecedents
• Cognitive Ability
• Implicit Theory of
Intelligence
• Need for Achievement
• Personality
• General Self-efficacy
• Self-esteem
Goal Orientation
Dimensions
• Learning
• Prove Performance
• Avoid Performance Proximal Consequences
• State Learning GO
• State Prove Performance GO
• State Avoid Performance GO
• Specific Self-efficacy
• Self-set Goal Level
• Learning Strategies
• Feedback Seeking
• State Anxiety
Distal Consequences
• Learning
• Academic Performance
• Task Performance
• Job Performance
Figure 1. Organizing framework for the variables examined. GO ⫽goal orientation.
131
GOAL ORIENTATION META-ANALYSIS
individuals who view their intelligence as fixed have lower levels
of general self-efficacy than individuals who view their intelli-
gence as malleable. If an entity theory of intelligence leads to low
levels of general self-efficacy and a PGO, then the PGO dimen-
sions will relate negatively to general self-efficacy. In addition,
Dweck (1989) argued that individuals with a strong LGO tend to
believe performance can be improved through effort. These beliefs
are facilitated by higher levels of self-efficacy, suggesting a pos-
itive relationship between self-efficacy and LGO. In contrast,
self-efficacy is likely to negatively relate to APGO, as low levels
of self-efficacy are positively related to intrusive thoughts.
Proximal Consequences of GO
In this section, we explore several variables believed to be
proximal outcomes of GO. We include state GO and the self-
regulatory constructs: task-specific self-efficacy, self-set goal
level, learning strategies, feedback seeking, and state anxiety.
These variables are believed to play key roles in directing and
sustaining task-related effort, so conceptually they serve as explan-
atory mechanisms for the GO– distal consequences relationships.
State GO
State GO describes the goal one has for a given situation (Ames
& Archer, 1988). It is conceptually similar to trait GO as it
represents one’s goal preferences in an achievement situation;
however, state GO is specific to the task and context at hand.
Accordingly, many researchers have experimentally induced a
certain GO for a specific task over a short time period (e.g.,
Stevens & Gist, 1997). Therefore, state GO is expected to be less
stable than trait GO and influenced by environmental conditions.
Like trait GO, state GO is believed to be multidimensional, con-
sisting of at least three dimensions: state LGO (SLGO), state
PPGO (SPPGO), and state APGO (SAPGO). Also consistent with
the idea that traits underlie states (Mischel & Shoda, 1995), each
state GO dimension is expected to relate to its corresponding trait
GO dimension.
Specific Self-Efficacy
Task-specific self-efficacy is an individual’s belief in his or her
capability to perform well on a task, given a specific set of
situational demands (A. Bandura, 1982). Unlike general self-
efficacy, task-specific self-efficacy is task and context specific.
Therefore, we conceptualized general self-efficacy as an anteced-
ent of GO and specific self-efficacy as an outcome of GO. We
expected trait GO to be more strongly related to general self-
efficacy than to specific self-efficacy, as the level of specificity
and stability are more consistent across these constructs. However,
we expected the pattern of relationships with the GO dimensions to
be the same.
Self-Set Goal Level
Both GO and self-set goal level involve the allocation of effort
in achievement-related situations. However, they are not synony-
mous constructs. Whereas GO research focuses on goal content,
goal-setting research focuses primarily on goal difficulty and spec-
ificity (Locke & Latham, 2002). Goal level refers to the difficulty
of the performance standard. In this study, we focus specifically on
self-set goals, because we are interested in self-regulatory pro-
cesses. We also focus on performance goals (e.g., to earn an A in
the class) as opposed to learning goals (e.g., to master the mate-
rial), because this is what is most often examined in the literature
(e.g., Chen et al., 2000; VandeWalle, Brown, Cron, & Slocum,
1999). Individuals with a strong LGO tend to be interested in
learning for its own sake and often view achievement situations as
a challenge. Therefore, they may be inclined to set more difficult
goals. Individuals with a strong PPGO wish to demonstrate their
competence to others (VandeWalle, 1996); thus, they are likely to
set high goals for themselves as they want to ensure they perform
well so they can look good to others. Conversely, individuals with
strong APGO tend to view achievement situations as threatening to
perceptions of their competence. They are concerned about not
letting others see them fail. Therefore, LGO and PPGO were
expected to positively correlate with self-set goal level, and APGO
was expected to negatively correlate with self-set goal level.
Learning Strategies
Another important outcome of GO is the learning strategies,
such as rehearsal, people use to enhance their own performance.
Research has shown that such strategies enhance the learning
process (Pintrich & de Groot, 1990; Volet, 1991). Research has
also shown students with learning goals engage in more effective
learning strategies than individuals with high levels of the PGO
dimensions (e.g., Ames & Archer, 1988; Dweck & Elliott, 1983;
Meece, Blumenfeld, & Hoyle, 1988; Miller, Behrens, Greene, &
Newman, 1993; Nolen, 1988). Moreover, researchers have spec-
ulated that high levels of the PGO dimensions are negatively
associated with learning strategies, because individuals with high
PGO are likely to take a more shallow approach to learning (Ford,
Smith, Weissbein, Gully, & Salas, 1998). However, the empirical
data have not always supported these propositions (Ford et al.,
1998).
Feedback Seeking
An important outcome in any organizational setting is the extent
to which people actively seek feedback to improve their perfor-
mance. VandeWalle and Cummings (1997) hypothesized that GO
influences individuals’ perceptions about the relative costs and
benefits associated with feedback seeking, which in turn relates to
actual feedback-seeking behaviors. For example, the costs associ-
ated with seeking feedback could potentially include a blow to
one’s ego (if the feedback is negative) as well as being perceived
by others as weak. The value of feedback is that the feedback
content will be useful for improving one’s subsequent perfor-
mance. VandeWalle and Cummings proposed that individuals with
high levels of LGO have a propensity to seek feedback, whereas
individuals with strong PPGO or APGO would be less inclined to
seek feedback. We speculated LGO would have a stronger positive
relationship with feedback seeking than PPGO, and APGO would
have a negative relationship with feedback seeking.
State Anxiety
An affective outcome of GO is state anxiety, or an aversive
emotional state of distress and uneasiness. In academic settings,
132 PAYNE, YOUNGCOURT, AND BEAUBIEN
state anxiety is frequently operationalized as “test anxiety” or
evaluation apprehension during an exam (Spielberger & Vagg,
1995). Both state anxiety (Covington, 1985) and APGO (Elliot &
Church, 1997) are grounded in the generalized need to avoid
failure or fear of failure and APGO. Thus, we expected APGO to
be positively related to state anxiety.
Distal Consequences of GO
In addition to the proximal consequences of GO, we also ex-
amined the more distal outcomes: learning, academic performance,
task performance, and job performance. Consistent with our orga-
nizing framework depicted in Figure 1, we expected trait GO to be
a distal antecedent of these outcomes working through more prox-
imal self-regulatory constructs. Therefore, we expected the rela-
tionships between trait GO and distal consequences to be weaker
than the relationships between trait GO and more proximal con-
sequences. We also examined how the state GO dimensions relate
to these same four outcomes. We expected the same pattern of
relationships predicted for trait GO to emerge for state GO; how-
ever, consistent with trait–state theory, we expected relationships
with state GO to be stronger than relationships with trait GO.
Learning and Academic Performance
GO describes behavioral tendencies when faced with
achievement-oriented tasks. We examined learning and academic
performance as separate outcomes, as academic performance is
broader than learning. Learning is the acquisition of declarative
and procedural knowledge typically assessed through performance
on a test or exam. Academic performance reflects how well an
individual performs on various academic-related tasks over a pe-
riod of time. It is an indicator of learning but can also be an
indicator of motivation, time management, and written communi-
cation skills, among other things. Academic performance is most
frequently operationalized as a final course grade or an overall
grade point average.
LGO is associated with a variety of adaptive thoughts and
behaviors. These include viewing failure as a learning experience,
persisting in the face of adversity, maintaining high levels of
self-efficacy, and setting high goals. LGO is also positively asso-
ciated with other self-regulatory behaviors such as planning and
goal setting (Sujan, Weitz, & Kumar, 1994), which facilitate
performance in academic domains. Given this pattern of relation-
ships, individuals with high levels of LGO are expected to perform
well on academic tasks. In contrast, individuals with high levels of
the PGO dimensions report experiencing thoughts unrelated to the
task (Diener & Dweck, 1978) or thoughts about escaping from the
task when they are engaging in task performance (Button et al.,
1996), which can inhibit effective performance (Kanfer & Acker-
man, 1996). Given that academic performance is a broader con-
struct determined by multiple predictors, we expected GO to relate
more strongly to learning than to academic performance.
Task and Job Performance
Two final outcomes of interest are task and job performance.
These constitute behavior in experimental tasks performed as a
part of a study conducted in the laboratory as well as performance
in the workplace. The pattern of relationships between GO and
task performance was expected to be similar to the pattern of
relationships hypothesized between GO and learning–academic
performance. For example, LGO is positively associated with
self-regulatory processes like goal setting and task persistence that
facilitate learning and performance in nonacademic domains as
well (Locke, Shaw, Saari, & Latham, 1981; Wood & Bandura,
1989). Thus, LGO was expected to have a positive relationship
with task performance, because acquiring the necessary knowledge
and skills to perform the task would facilitate performance. Like-
wise, because the PGO dimensions are associated with intrusive
thoughts that inhibit performance, they were expected to relate
negatively to task and job performance.
Similar to learning and academic performance, task perfor-
mance is conceptualized as a part of job performance. In other
words, job performance is determined by more than just task
performance. Further, task performance is primarily operational-
ized as performance on an experimental task in a laboratory
setting, whereas job performance can be conceived as task perfor-
mance in the field. Previous meta-analytic investigations have
found stronger effects on performance in the lab than in the field
(e.g., Stajkovic & Luthans, 1998). For these reasons, GO was
expected to have stronger relationships with task performance than
with job performance.
Incremental Validity
One way to assess the value of GO is to examine the extent to
which it predicts important outcomes above and beyond well-
established predictors of these outcomes. For example, cognitive
ability is arguably the best predictor of job performance (Hunter &
Hunter, 1984). At the same time, nonability predictors (e.g., per-
sonality variables) have been shown to predict incremental validity
above and beyond cognitive ability (F. L. Schmidt & Hunter,
1998). Thus, trait GO is even more valuable to organizational
researchers and practitioners if it demonstrates incremental valid-
ity. Accordingly, we tested the extent to which trait GO predicts
distal consequences above and beyond cognitive ability and the
Big Five.
Method
Identification of Studies
We conducted a comprehensive literature review to identify
published and unpublished studies containing trait or state mea-
sures of the learning, prove, and avoid dimensions of GO. First, we
conducted a computerized search of the PsycINFO, PsycFIRST,
Dissertation Abstracts, and ABI Inform databases using keywords
such as GO,mastery GO,LGO,PGO,ego GO,achievement goals,
and their variants. Second, we conducted a manual search of
journals that routinely publish GO research between 1993 and
2002. Third, we scanned Society for Industrial and Organizational
Psychology and the Academy of Management conference pro-
grams for unpublished papers between 1996 and 2002. Fourth, we
contacted key researchers in the field to solicit unpublished data
and manuscripts. Finally, we used a “snowballing” technique to
identify source studies that were cited in the reference section of
each study. This search process yielded 469 manuscripts.
133
GOAL ORIENTATION META-ANALYSIS
Inclusion Criteria
We developed several decision rules to aid in deciding which
studies to include in the final data set. First, because the focus of
this meta-analysis concerned the value of trait or state measures of
GO to organizational researchers, we limited the samples to studies
of adults in educational and occupational settings, deliberately
excluding child or adolescent samples and studies examining
sports-related tasks. Second, we excluded studies that experimen-
tally manipulated or induced state GO as opposed to measuring it.
2
Third, we excluded studies that measured GO at the group level of
analysis. Fourth, studies had to report sample sizes along with
correlations or sufficient information to compute a correlation (cf.
Arthur, Bennett, & Huffcutt, 2001; Wolfe, 1986).
Coding of Studies
Stephanie C. Payne and Satoris S. Youngcourt or J. Matthew
Beaubien independently coded each study for sample size, effect
sizes, and measurement reliability. Meetings were held to review
each article and discuss discrepancies, all of which were resolved
by consensus. When a two-dimensional measure (e.g., Button et
al.’s, 1996, measure) of GO was used, the correlations with PGO
were coded as PPGO, as the items tended to reflect this dimension
more so than APGO.
Final Data Set
Nonindependence. The data were examined for nonindepen-
dence, which occurs when multiple data points come from the
same sample of participants. However, decisions about noninde-
pendence are not solely based on whether the same sample is used
but must also take into account whether the same variable or
construct is being assessed (Arthur et al., 2001). For example, data
points based on multiple measures of the same criterion (e.g., two
measures of state anxiety; Chen et al., 2000) for the same sample
were considered to be nonindependent and were formed into a
single data point using calculations set forth by Hunter and
Schmidt (1990). Data points based on multiple time periods of the
same or similar criterion for the same sample were also considered
to be nonindependent and were combined into a single composite
data point. Taken together, these decision rules yielded 178 inde-
pendent samples derived from 141 studies examining trait GO.
Further, these decision rules resulted in 19 independent samples
derived from 16 studies examining state GO. All references that
included codable data are marked in the reference section with an
asterisk.
Outliers. We computed the sample-adjusted meta-analytic de-
viancy statistic to identify potential outliers in each predictor–
criterion relationship (Arthur et al. 2001; Huffcutt & Arthur,
1995). Following Cortina’s (2003) recommendation to discard
outliers only if there is overwhelming empirical or methodological
justification, we reviewed the flagged studies on a case-by-case
basis. We identified outliers for the relationships between GO and
implicit theory of intelligence, specific self-efficacy, learning strat-
egies, and the intercorrelations between the GO dimensions.
3
We
report all analyses with the outliers removed.
Analytical Techniques
Preliminary analyses. Data analysis was performed using
Arthur et al.’s (2001) SAS PROC MEANS meta-analysis program,
an implementation of Hunter and Schmidt’s (1990) psychometric
meta-analysis technique, to compute sample-weighted correla-
tions. In addition to correcting for sampling error, we corrected for
both predictor and criterion unreliability. Because reliability infor-
mation was not always available for every variable in every study,
we used an artifact distribution based on the available study
information (see the Appendix). Corrections for range restriction
were not made because of insufficient information.
Incremental validity. We examined the incremental validity of
trait GO on job performance over and above cognitive ability and
the Big Five. We chose this outcome because there were meta-
analytic estimates of the relationships between cognitive ability
and the Big Five for this outcome, and these values are necessary
to perform the analyses. Specifically, we conducted a series of
regressions using data from meta-correlation matrices composed
of rho values from multiple meta-analyses (Viswesvaran & Ones,
1995). The values for the relationships between cognitive ability
and the Big Five were obtained from Ackerman and Heggested
(1997). The relationship between cognitive ability and job perfor-
mance came from Hunter and Hunter (1984). The relationships
between the Big Five and job performance came from Barrick and
Mount (1991). The interrelationships among the Big Five variables
came from Ones (1994). All other values were from our meta-
2
We chose not to include studies that manipulated state GO, because
most of the studies that induce GO pit LGO against a global PGO using a
between-subjects design and they do not integrate conceptually or empir-
ically into our study. First, conceptually pitting LGO against PGO implies
GO is a bipolar construct in which individuals can only be high on one.
This conceptualization was questioned by Button et al. (1996) over 10
years ago on the basis of the idea that an individual can have multiple
goals. Second, empirical research supports the multidimensionality of GO
(e.g., VandeWalle, 1996, 1997). Third, the data and results of studies
inducing GO are quite different from data included in the present meta-
analysis. Most frequently, means are generated for each experimental
group (LGO and PGO) and the effect size available is one dvalue (e.g., the
mean for the LGO group subtracted from the mean for the PGO group,
divided by the pooled standard deviation). This value is interpreted as the
advantage (or disadvantage) of one inducement over the other on a given
outcome variable (e.g., task performance). These data are not easily inte-
grated with our meta-analytic results reported as rho values for each
dimension with each variable of interest. Finally, the only variable consis-
tently examined across these studies that has not been previously meta-
analyzed is task performance.
3
The outliers for temporal stability was Amabile, Hill, Hennessey, and
Tighe (1994; student and adult samples). Outliers for the intercorrelations
between the different GO dimensions were Amabile et al. (1994; student
and adult samples); Tuckey, Brewer, and Williamson (2002; student sam-
ple); Dykeman (1994); Mangos, Steele-Johnson, and Heintz (2001); and
Tan (2002; Midgley scale). Outliers for implicit theory of intelligence were
Button et al. (1996; Studies 2, 3, and 4) and Hofmann (1993b; Studies 1
and 2). Outliers for specific self-efficacy were Pintrich, Smith, Garcia, and
McKeachie (1993); Brink and Thomas (2001); Pintrich, Zusho, Schiefele,
and Pekrun (2001; German and U.S. samples); and Hoover, Steele-
Johnson, Beauregard, and Schmidt (1999). The outlier for learning strate-
gies was Simmons (1997). Analyses with outliers are available from
Satoris S. Youngcourt.
134 PAYNE, YOUNGCOURT, AND BEAUBIEN
analytic data. Consistent with Viswesvaran and Ones’s (1995)
recommendation, we used the harmonic mean for each pair of
relationships within the meta-correlation matrix.
Results
Tables 1–5 display the number of effect sizes included in the
analysis (k), total sample size across studies (N), sample-weighted
mean correlations (mean r), estimated true mean correlations (),
and estimated standard deviations for the true mean correlations
(SD). Also included is the percentage of variance attributable to
sampling error (% variance SE), the percentage of variance ex-
plained by artifacts, 95% confidence intervals (CIs), and 95%
credibility intervals. CIs are used to assess the accuracy of the
estimate of the mean effect size (Whitener, 1990). CIs estimate the
extent to which sampling error remains in the sample-size-
weighted mean effect size, whereas the variance explained by
statistical artifacts, the credibility interval, and the SDwere used
as indicators of the presence of moderators or to determine
whether a given effect is dependent on the situation (i.e., Hunter &
Schmidt, 1990; Whitener, 1990). We used Cohen’s (1988) con-
ventions of .1, .3, and .5 when interpreting effect sizes as small,
medium, and large, respectively.
Temporal Stability
Consistent with other reliability generalization studies (e.g., Yin
& Fan, 2000), we did not correct mean coefficients of stability for
internal consistency unreliability. Instead, we simply examined the
sample-weighted mean correlations for each dimension. Overall,
the coefficient of stability estimates for trait GO were high, indi-
cating a moderate degree of stability over time. The sample-
weighted mean rwas .66 (k⫽20) for LGO, .70 (k⫽16) for
PPGO, and .73 (k⫽4) for APGO. These results indicate all three
GO dimensions are stable at time intervals ranging from 1 to 14
weeks (M⫽7.01, SD ⫽3.89). We examined the length of time
between administrations as a continuous moderator of the test–
retest relationships. The relationships between the time interval
and coefficients of stability were negative for LGO (r⫽–.20),
PPGO (r⫽–.29), and APGO (r⫽–.74). In other words, the
longer the time frames between administrations, the smaller the
coefficients of stability.
Relationships Among the GO Dimensions
We examined the interrelationships among the trait dimensions
as well as the state dimensions (see Table 1). PPGO was positively
correlated with LGO (⫽.15 for trait and .30 for state). Consistent
with previous speculation, LGO was negatively related to APGO
(⫽–.23 for trait and –.09 for state), and the two performance
dimensions were positively related to one another (⫽.40 for trait
and .78 for state).
Antecedents of GO
We examined 10 potential antecedents of GO. These results are
depicted in Table 2.
Cognitive ability. We found negligible relationships between
cognitive ability and LGO (⫽.04), PPGO (⫽–.02), and APGO
(⫽–.09). Although only the CI for PPGO contained zero, given
the extremely low effect sizes, GO and cognitive ability appear to
be independent.
Implicit theory of intelligence. All studies examining the im-
plicit theory of intelligence were coded such that entity theory was
scored higher on the bipolar measure. Consistent with Dweck’s
(1986) theory, entity theory of intelligence had a small negative
true mean correlation with LGO (⫽–.12) and a small positive
true mean correlation with PPGO (⫽.10), with neither CI
containing zero. There was also a small positive relationship
between APGO and entity theory of intelligence (⫽.09), but
only two studies reported this relationship.
Need for achievement. Consistent with our expectation, need
for achievement correlated positively with LGO (⫽.48) and
negatively with APGO (⫽–.15). Contrary to expectation, need
for achievement had virtually no relationship with PPGO (⫽
.03), as the CI included zero.
Personality characteristics. Next, we examined the relation-
ships among the three GO dimensions and the Big Five. By far,
conscientiousness was the most studied personality characteristic
in relation to GO. As expected, conscientiousness was positively
related to LGO (⫽.32) and negatively related to APGO (⫽
–.18). Conscientiousness was unrelated to PPGO (⫽.03), with a
CI containing zero.
The results for the other four personality characteristics were
quite similar to those for conscientiousness. LGO related posi-
Table 1
Intercorrelations Among the Goal Orientation Dimensions
Examined relationship kN
Sample-
weighted
mean rSD
% variance
SE
% variance
artifacts 95% CI
95% credibility
interval
TLGO–PPGO 148 34,039 .12 .15 .17 18.08 18.31 .11:.13 ⫺.19:.49
TLGO–TAPGO 48 10,636 ⫺.19 ⫺.23 .23 10.72 10.95 ⫺.20:⫺.17 ⫺.69:.23
TPPGO–TAPGO 48 10,643 .31 .40 .15 20.04 21.89 .30:.33 .10:.70
SLGO–SPPGO 10 1,091 .24 .30 .23 20.11 20.68 .18:.29 ⫺.15:.74
SLGO–SAPGO 2 185 ⫺.07 ⫺.09 .51 5.60 5.61 ⫺.22:.07 ⫺1.00:.92
SPPGO–SAPGO 2 185 .65 .78 .42 2.80 4.72 .57:.73 ⫺.04:1.00
Note. TLGO ⫽trait learning goal orientation; PPGO ⫽prove performance goal orientation; TAPGO ⫽trait avoid performance goal orientation;
TPPGO ⫽trait prove performance goal orientation; SLGO ⫽state learning goal orientation; SPPGO ⫽state prove performance goal orientation;
SAPGO ⫽state avoid performance goal orientation; k⫽total number of effect sizes included in the analysis; N⫽total sample size across studies; %
variance SE ⫽percentage of variance attributable to sampling error; % variance artifacts ⫽percentage of variance attributable to unreliability in the
measures; CI ⫽confidence interval.
135
GOAL ORIENTATION META-ANALYSIS
tively to extraversion (⫽.29), openness to experience (⫽.44),
agreeableness (⫽.19), and emotional stability (⫽.18). APGO
related negatively to extraversion (⫽–.30), openness to experi-
ence (⫽⫺.25), agreeableness (⫽–.19), and emotional stability
(⫽–.37). PPGO was unrelated to extraversion (⫽–.03, with a
CI containing zero), openness to experience (⫽–.06), and
agreeableness (⫽–.07). Emotional stability, however, yielded a
negative relationship with PPGO (⫽–.32).
Self-esteem. Self-esteem related positively to LGO (⫽.38)
and negatively to PPGO (⫽–.11) and APGO (⫽–.39). It
should be noted that both of the PGO CIs contained zero.
General self-efficacy. Consistent with our expectations, we
found general self-efficacy to have a strong positive relationship
with LGO (⫽.71) and a strong negative relationship with APGO
(⫽–.61). Whereas general self-efficacy was also negatively
related to PPGO (⫽–.08), the effect size was much smaller and
the CI approached zero.
Proximal Consequences of GO
We examined eight proximal consequences of trait GO. The
results are shown in Table 3.
State GO. Consistent with expectations, corresponding trait
and state GO dimensions exhibited strong positive relationships
with one another (LGO–SLGO, ⫽.55; PPGO–SPPGO, ⫽.58;
APGO–SAPGO, ⫽.55). Relationships between the mismatched
trait and state GO dimensions were smaller, ranging from –.07 to
.45. Of note, SLGO had a relatively strong positive relationship
with PPGO (.45). This is much stronger than the relationship
Table 2
Antecedents of Goal Orientation
Examined relationship kN
Sample-
weighted
mean rSD
% variance
SE
% variance
artifacts 95% CI
95% credibility
interval
Cognitive ability
LGO 51 10,873 .04 .04 .04 58.63 81.25 .02:.06 ⫺.03:.12
PPGO 49 10,616 ⫺.02 ⫺.02 .03 73.74 81.67 ⫺.04:⫺.00 ⫺.09:.04
APGO 16 3,497 ⫺.09 ⫺.09 0 36.16 113.13 ⫺.13:⫺.06 ⫺.09:⫺.09
Implicit theory of intelligence
LGO 11 2,861 ⫺.10 ⫺.12 .06 56.49 57.00 ⫺.14:⫺.07 ⫺.25:.00
PPGO 12 2,967 .08 .10 .10 37.17 37.56 .05:.12 ⫺.10:.30
APGO 2 494 .07 .09 .23 9.97 10.01 ⫺.01:.16 ⫺.37:.55
Need for achievement
LGO 20 4,709 .38 .48 .16 16.12 17.95 .36:.41 .17:.80
PPGO 19 4,567 .02 .03 .16 21.92 21.93 ⫺.01:.05 ⫺.28:.33
APGO 5 974 ⫺.12 ⫺.15 .15 28.21 28.37 ⫺.18:⫺.06 ⫺.44:.13
Agreeableness
LGO 9 2,448 .15 .19 .03 81.95 83.07 .11:.19 .12:.25
PPGO 9 2,448 ⫺.06 ⫺.07 .10 37.27 37.40 ⫺.10:⫺.02 ⫺.27:.13
APGO 5 1,405 ⫺.15 ⫺.19 .04 75.89 76.74 ⫺.20:⫺.09 ⫺.27:⫺.11
Conscientiousness
LGO 12 3,066 .26 .32 .11 31.63 33.07 .22:.29 .11:.53
PPGO 12 3,066 .04 .06 .03 87.80 87.97 .01:.08 ⫺.00:.11
APGO 6 1,732 ⫺.14 ⫺.18 .04 72.38 73.26 ⫺.19:⫺.10 ⫺.27:⫺.09
Emotional stability
LGO 11 3,042 .14 .18 .04 78.66 79.64 .11:.17 .10:.25
PPGO 10 2,946 ⫺.26 ⫺.32 0 122.44 134.05 ⫺.29:⫺.22 ⫺.32:⫺.32
APGO 5 1,416 ⫺.29 ⫺.37 0 26.23 273.72 ⫺.32:⫺.24 ⫺.37:⫺.37
Extraversion
LGO 12 3,215 .24 .29 .10 33.27 33.82 .21:.27 .10:.48
PPGO 11 2,776 .03 ⫺.03 .04 78.58 78.62 ⫺.01:.06 ⫺.05:.11
APGO 5 1,404 ⫺.24 ⫺.30 0 635.13 640.88 ⫺.29:⫺.19 ⫺.30:⫺.30
Openness to experience
LGO 16 4,359 .34 .44 .09 36.59 42.51 .31:.37 .27:.61
PPGO 16 4,359 ⫺.05 ⫺.06 .12 30.43 30.51 ⫺.08:⫺.02 ⫺.30:.17
APGO 7 2,098 ⫺.19 ⫺.25 .05 63.58 66.19 ⫺.23:⫺.14 ⫺.35:⫺.14
Self-esteem
LGO 11 2,908 .32 .38 .18 11.94 12.25 .29:.36 .03:.73
PPGO 11 2,908 ⫺.09 ⫺.11 .07 54.92 55.13 ⫺.13:⫺.05 ⫺.24:.02
APGO 3 945 ⫺.31 ⫺.39 .18 10.55 10.60 ⫺.37:⫺.26 ⫺.75:⫺.03
General self-efficacy
LGO 9 2,366 .56 .71 .06 42.48 51.74 .53:.59 .60:.82
PPGO 9 2,366 ⫺.06 ⫺.08 .11 33.36 33.48 ⫺.10:⫺.02 ⫺.30:.14
APGO 3 944 ⫺.47 ⫺.61 0 100.52 117.38 ⫺.52:⫺.42 ⫺.61:⫺.61
Note. LGO ⫽learning goal orientation; PPGO ⫽prove performance goal orientation; APGO ⫽avoid performance goal orientation; k⫽total number
of effect sizes included in the analysis; N⫽total sample size across studies; % variance SE ⫽percentage of variance attributable to sampling error; %
variance artifacts ⫽percentage of variance attributable to unreliability in the measures; CI ⫽confidence interval.
136 PAYNE, YOUNGCOURT, AND BEAUBIEN
between LGO and PPGO (.15) or the relationship between SLGO
and SPPGO (.30).
Specific self-efficacy. Consistent with expectations, task-
specific self-efficacy was positively correlated with LGO (⫽.37)
and negatively correlated with APGO (⫽–.26). Contrary to our
expectation, PPGO was virtually unrelated to task-specific self-
efficacy (⫽.03).
Self-set goal level. As expected, we found a positive relation-
ship between self-set goal level and LGO (⫽.19) and a negative
relationship of approximately the same magnitude with APGO
(⫽–.17). PPGO yielded a trivial negative relationship with
self-set goal (⫽–.04), with a CI containing zero.
Learning strategies. Results reveal positive true mean corre-
lations between learning strategies and LGO (⫽.49) and PPGO
(⫽.16) and a near zero relationship with APGO (⫽.03, with
a CI including zero).
Feedback seeking. The relationships between GO and feed-
back seeking were markedly different for the three GO dimen-
sions. Specifically, feedback seeking was positively related to
LGO (⫽.20), negatively related to APGO (⫽–.22), and
unrelated to PPGO (⫽–.01).
State anxiety. For state anxiety, results reveal a negative true
mean correlation with LGO (⫽–.10) and positive true mean
correlations with PPGO (⫽.19) and APGO (⫽.36), with none
of the CIs containing zero.
Distal Consequences of Trait GO
We examined four distal consequences or outcomes of trait GO.
Our results appear in Table 4. It is important to note that there are
relatively equal number of studies examining learning and aca-
demic performance and far fewer studies looking at task perfor-
mance—particularly task performance in the field of job perfor-
mance. Thus, we are more confident about findings concerning
learning and academic performance than findings for task and job
performance.
Learning and academic performance. Consistent with expec-
tations, learning was positively related to LGO (⫽.16), not
related to PPGO (⫽–.01), and negatively related to APGO (⫽
–.17), with both the PPGO and APGO CIs containing zero. A
relatively similar pattern emerged for academic performance: a
positive relationship for LGO (⫽.16) and no relationships for
Table 3
Proximal Consequences of Goal Orientation
Examined
relationship kN
Sample-
weighted
mean rSD
% variance
SE
% variance
artifacts 95% CI
95% credibility
interval
State LGO
LGO 19 3,373 .45 .55 .13 22.78 28.61 .43:.48 .30:.81
PPGO 17 2,777 .36 .45 .15 30.78 30.81 ⫺.00:.07 ⫺.24:.33
APGO 7 1,218 ⫺.05 ⫺.07 .13 33.40 33.48 ⫺.11:.00 ⫺.33:.20
State PPGO
LGO 17 2,777 .11 .13 .11 42.92 43.20 .07:.15 ⫺.08:.34
PPGO 18 2,887 .47 .58 .13 23.32 28.16 .44:.50 .32:.85
APGO 7 918 .14 .17 0 281.02 283.49 .07:.20 .17:.17
State APGO
LGO 7 918 ⫺.16 ⫺.19 .11 48.14 48.64 ⫺.22:⫺.10 ⫺.40:.02
PPGO 7 918 .17 .22 0 99.30 101.05 .11:.24 .22:.22
APGO 7 918 .45 .55 .15 23.90 27.46 .39:.50 .25:.85
Specific self-efficacy
LGO 49 10,649 .31 .37 .10 34.56 37.24 .29:.33 .17:.56
PPGO 44 9,266 .03 .03 .12 31.10 31.12 .01:.05 ⫺.21:.28
APGO 8 1,882 ⫺.21 ⫺.26 0 127.51 130.23 ⫺.25:⫺.16 ⫺.26:⫺.26
Self-set goals
LGO 21 4,751 .16 .19 .11 33.08 33.28 .13:.19 ⫺.02:.40
PPGO 20 4,265 ⫺.03 ⫺.04 .10 38.90 38.92 ⫺.06:⫺.00 ⫺.24:.16
APGO 7 1,227 ⫺.14 ⫺.17 0 363.25 363.95 ⫺.19:⫺.08 ⫺.17:⫺.17
Learning strategies
LGO 32 6,859 .39 .49 .14 20.13 23.88 .37:.41 .21:.77
PPGO 23 4,994 .13 .16 .10 41.58 42.87 .10:.16 ⫺.03:.36
APGO 5 1,106 .02 .03 .31 6.53 6.53 ⫺.03:.08 ⫺.59:.65
Feedback seeking
LGO 12 2,381 .20 .24 .14 26.32 28.76 .16:.24 ⫺.02:.51
PPGO 10 1,847 ⫺.01 ⫺.01 .16 24.67 24.68 ⫺.05:.04 ⫺.32:.30
APGO 6 987 ⫺.22 ⫺.27 .23 13.57 14.98 ⫺.28:⫺.16 ⫺.72:.18
State anxiety
LGO 16 3,254 ⫺.09 ⫺.10 .06 66.34 66.77 ⫺.12:⫺.05 ⫺.22:.01
PPGO 15 2,915 .16 .19 .06 61.44 61.45 .13:.20 .07:.32
APGO 7 1,241 .31 .36 .15 21.70 22.56 .26:.36 .07:.66
Note. LGO ⫽learning goal orientation; PPGO ⫽prove performance goal orientation; APGO ⫽avoid performance goal orientation; k⫽total number
of effect sizes included in the analysis; N⫽total sample size across studies; % variance SE ⫽percentage of variance attributable to sampling error; %
variance artifacts ⫽percentage of variance attributable to unreliability in the measures; CI ⫽confidence interval.
137
GOAL ORIENTATION META-ANALYSIS
PPGO (⫽.02) or APGO (⫽–.06), with all three dimension CIs
containing zero. Contrary to our expectation, relationships with
learning were not substantially larger than relationships with aca-
demic performance.
4
Task and job performance. There were no meaningful rela-
tionships between task performance and LGO (⫽.05) or PPGO
(⫽–.01), as both CIs contained zero. A small negative relation-
ship emerged with APGO (⫽–.13). Job performance related
positively to LGO (⫽.18) and PPGO (⫽.11), but both CIs
contained zero. No studies examined APGO and job performance.
Distal Consequences of State GO
The results for state GO with the same four distal consequences
are depicted in Table 5.
Learning and academic performance. Only SLGO could be
examined with learning, as no studies examined learning with
SAPGO and only one study examined SPPGO. Like LGO, SLGO
was positively related to learning (⫽.31), but even these results
should be interpreted with caution, as they are based on only two
studies. For academic performance, results yielded no meaningful
relationships with SLGO (⫽–.01) and SPPGO (⫽–.02). No
studies examined academic performance and SAPGO. Consistent
with our expectation, SLGO had a stronger relationship with
learning than academic performance. Also, SLGO had a stronger
relationship with learning (.31) than LGO (.16), but LGO had a
stronger relationship with academic performance (.16) than SLGO
(–.01).
Task and job performance. Contrary to expectation, SLGO
yielded no relationship with task performance (⫽.06), whereas
PPGO yielded a small positive relationship (⫽.16). Again,
however, these results must be interpreted with caution, as only
three studies contributed to each of these rho values. No studies
examined SAPGO and task performance. Small positive relation-
ships emerged between job performance and SLGO (⫽.22) and
SPPGO (⫽.09). Only one study examined SAPGO and job
performance. Consistent with expectation, SPPGO had a stronger
relationship with task performance (⫽.16) than did PPGO (⫽
–.01), and SLGO had a slightly stronger relationship with job
performance (⫽.22) than did LGO (⫽.18). Contrary to
expectation, both trait and state LGO had stronger relationships
with job performance (⫽.18 and ⫽.22, respectively) than task
performance (⫽.05 and ⫽.06, respectively). On the other
hand, SPPGO had a slightly stronger relationship with task per-
formance (⫽.16) than with job performance (⫽.09).
Incremental validity. Finally, we examined the incremental
validity of trait GO on job performance over and above cognitive
ability and the Big Five. The complete meta-correlation matrix is
shown in Table 6. GO did predict a significant amount of incre-
mental validity in job performance (⌬R
2
⫽.04, p⬍.01; see Table
7) and LGO that is largely responsible for this variance (⫽.23,
p⬍.05).
Discussion
In this article, we meta-analytically examine the relationships
among three GO dimensions, the temporal stability of the trait-
based dimensions, and relationships between the dimensions and
presumed antecedents and consequences of GO. Whereas some
results confirm theory and previously established findings, others
do not.
4
We examined the academic performance measure as a potential mod-
erator; however, the validities did not vary substantially for the two
primary measures: grade and grade point average.
Table 4
Distal Consequences of Trait Goal Orientation
Examined relationship kN
Sample-
weighted
mean rSD
% variance
SE
% variance
artifacts 95% CI
95% credibility
interval
Learning
LGO 43 8,676 .12 .16 .05 75.93 79.70 .09:.14 .06:.25
PPGO 38 7,598 ⫺.01 ⫺.01 .09 52.02 52.04 ⫺.03:.01 ⫺.19:.17
APGO 13 2,856 ⫺.13 ⫺.17 .14 28.45 30.30 ⫺.16:⫺.09 ⫺.45:.11
Academic performance
LGO 47 10,296 .12 .16 .09 49.59 52.44 .10:.14 ⫺.01:.33
PPGO 44 9,628 .01 .02 .07 64.68 64.73 ⫺.01:.03 ⫺.11:.15
APGO 12 2,320 ⫺.05 ⫺.06 .00 104.74 104.79 ⫺.09:⫺.01 ⫺.06:⫺.06
Task performance
LGO 25 4,400 .04 .05 .08 54.92 54.96 .01:.07 .11:.21
PPGO 24 4,182 ⫺.00 ⫺.01 .08 60.25 60.25 ⫺.03:.03 ⫺.15:.14
APGO 4 703 ⫺.11 ⫺.13 .00 127.01 128.00 ⫺.18:⫺.04 ⫺.13:⫺.13
Job performance
LGO 7 1,133 .15 .18 .10 46.51 46.96 .09:.21 ⫺.01:.38
PPGO 7 1,133 .09 .11 .14 33.48 33.60 .03:.14 ⫺.16:.37
APGO 1 — — — — — — — —
Note. Dashes indicate that values could not be calculated. LGO ⫽learning goal orientation; PPGO ⫽prove performance goal orientation; APGO ⫽avoid
performance goal orientation; k⫽total number of effect sizes included in the analysis; N⫽total sample size across studies; % variance SE ⫽percentage
of variance attributable to sampling error; % variance artifacts ⫽percentage of variance attributable to unreliability in the measures; CI ⫽confidence
interval.
138 PAYNE, YOUNGCOURT, AND BEAUBIEN
Temporal Stability of Trait GO
Researchers have called for an examination of the temporal
stability of trait GO to better understand its influence on organi-
zational interventions such as training (e.g., Salas & Cannon-
Bowers, 2001). We discovered the three dimensions were quite
stable over the short term, as mean coefficients of stability were
comparable with those calculated for the Big Five (Viswesvaran &
Ones, 2000). However, the longer the time interval, the weaker the
coefficient of stability, undermining the stability of trait GO. That
said, few studies have examined the stability of trait GO beyond
the length of one college semester, so the long-term stability of
trait GO remains unclear. In addition, when the time period of a
study coincides with the beginning and end of the semester, time
Table 5
Distal Consequences of State Goal Orientation
Examined relationship kN
Sample-
weighted
mean rSD
% variance
SE
% variance
artifacts 95% CI
95% credibility
interval
Learning
SLGO 2 567 .25 .31 .00 — — .17:.33 .31:.31
SPPGO 1 — — — — — — — —
SAPGO 0 — — — — — — — —
Academic performance
SLGO 4 745 ⫺.01 ⫺.01 .00 644.48 644.50 ⫺.08:.07 ⫺.01:⫺.01
SPPGO 4 745 ⫺.02 ⫺.02 .00 164.18 164.18 ⫺.09:.05 ⫺.02:⫺.02
SAPGO 0 — — — — — — — —
Task performance
SLGO 3 308 .05 .06 .00 275.73 275.99 ⫺.06:.16 .06:.06
SPPGO 3 308 .14 .16 .00 808.71 808.83 .03:.25 .16:.16
SAPGO 1 — — — — — — — —
Job performance
SLGO 3 511 .18 .22 .00 1,212.56 1,234.35 .10:.26 .22:.22
SPPGO 3 511 .07 .09 .00 337.36 338.04 ⫺.02:.16 .09:.09
SAPGO 1 — — — — — — — —
Note. Dashes indicate that values could not be calculated. SLGO ⫽state learning goal orientation; SPPGO ⫽state prove performance goal orientation;
SAPGO ⫽state avoid performance goal orientation; k⫽total number of effect sizes included in the analysis; N⫽total sample size across studies; %
variance SE ⫽percentage of variance attributable to sampling error; % variance artifacts ⫽percentage of variance attributable to unreliability in the
measures; CI ⫽confidence interval.
Table 6
Meta-Correlation Matrix
Variable 1 2 3 4 5 6 7 8 9 10
1. Cognitive ability —
2. Openness .33
a
—
185
3. Conscientiousness .02
a
⫺.06
b
—
1,617 1,055
4. Extraversion .08
a
.17
b
.00
b
—
455 603 1081
5. Agreeableness .01
a
.11
b
.27
b
.17
b
—
157 611 474 558
6. Emotional stability .15
a
.16
b
.26
b
.19
b
.25
b
—
206 603 835 620 741
7. Learning GO .04 .44 .32 .29 .19 .18 —
213 272 256 268 272 277
8. Prove performance GO ⫺.02 ⫺.06 .06 ⫺.03 ⫺.07 ⫺.32 .15 —
217 272 256 252 272 295 230
9. Avoid performance GO ⫺.09 ⫺.25 ⫺.18 ⫺.30 ⫺.19 ⫺.37 ⫺.23 .40 —
219 300 289 281 281 283 222 222
10. Job performance .45
c
⫺.03
d
.23
d
.10
d
.06
d
.07
d
.18 .11 ⫺.06 —
515 172 140 139 144 134 162 162 124
e
Note. Values were obtained from the current meta-analytic data, unless otherwise noted. Values under each rho are the grand mean. M⫽0, SD ⫽1. GO ⫽
goal orientation.
a
Values obtained from Ackerman and Heggested (1997).
b
Values obtained from Ones (1994).
c
Values obtained from Hunter and Hunter
(1984).
d
Values obtained from Barrick and Mount (1991).
e
k⫽1.
139
GOAL ORIENTATION META-ANALYSIS
period is confounded with systematic changes in the learning
environment. Thus, variability in GO may be due to situational
changes (e.g., final exams, papers due, etc.), which may have
created a strong PGO situation.
Relationships Among the GO Dimensions
Despite the general belief that learning and prove dimensions
are not significantly related to each other (e.g., Button et al., 1996;
Heyman & Dweck, 1992; Roberts, Treasure, & Kavussanu, 1996),
our findings reveal a small positive correlation between them,
undermining Dweck’s (1986) perspective. This means researchers
should not always assume these two dimensions will relate differ-
entially to various outcomes. Perhaps the recent bifurcation of
LGO into approach and avoid dimensions will shed further light on
these results.
The intercorrelations among the dimensions revealed a small
positive relationship between LGO and PPGO and a negative
relationship between LGO and APGO. Prove and avoid LGO
scales may provide more interpretable relationships between the
learning and performance dimensions. Also, individuals who are
high in PPGO are also likely to be high in APGO. This pattern of
relationships held for both trait and state GO.
Given the multidimensional nature of GO, it may be meaningful
to examine how different combinations of these dimensions within
as well as across trait and state operationalizations relate to rele-
vant criterion variables. Our research shows that trait and state
operationalizations of the GO dimensions tend to operate quite
similarly; thus, a match between these operationalizations may be
most beneficial (e.g., high LGO with high SLGO); however,
empirical research is needed to confirm this speculation.
Antecedents of GO
Consistent with Dweck’s (1986) work, there was virtually no
relationship between cognitive ability and any of the three GO
dimensions. Thus, highly intelligent individuals are equally likely
to hold strong learning, prove, and avoid orientations. A lack of
relationship between these variables has implications for selection,
as it reduces any concerns about multicollinearity and suggests GO
could predict performance above and beyond cognitive ability. In
fact, after combining correlations from multiple meta-analyses, we
showed that trait LGO predicted job performance above and be-
yond cognitive ability and the Big Five. Thus, trait LGO may be of
particular interest to organizational researchers.
Consistent with Dweck’s (1986) theorizing, entity theory of
intelligence was negatively correlated with LGO and positively
correlated with both PGO dimensions. Contrary to Dweck’s (1986)
perspective, the effect sizes were very small, providing little evi-
dence for Dweck’s (1986) view that implicit theories are the
primary underlying antecedent of GO. One explanation is that our
findings may reflect second-order sampling error in that the rela-
tionships we calculated are based on 12 studies or fewer. A second
explanation concerns the measurement of implicit theory of intel-
ligence. All of the studies included in our study measured this
construct with a bipolar measure. Separate scales for each theory
(e.g., Dweck, 1999) might yield stronger correlations with GO, as
such measures are more consistent with GO measures; however,
initial empirical results indicate otherwise (Elliot & McGregor,
2001).
Consistent with a hierarchical model of approach and avoidance
achievement motivation (Elliot & Church, 1997), need for
achievement related positively and strongly to LGO and negatively
to APGO. It did not, however, relate to PPGO. Of note, need for
achievement related more strongly to LGO than the broader con-
struct conscientiousness. Although this relationship is strong, it
demonstrates LGO is not synonymous with need for achievement.
The GO dimensions seem to be related to configural patterns of
personality characteristics. LGO is associated with high openness,
extraversion, and conscientiousness. PPGO is associated primarily
with low emotional stability, and APGO is associated with low
emotional stability and extraversion. This finding suggests profiles
of the Big Five can be associated with each of the GO dimensions.
All together, these results reveal nontrivial relationships be-
tween GO and the Big Five, with the strongest positive relation-
ships between LGO and openness to experience and conscientious-
ness. Yet our analyses confirm that the GO dimensions are not
completely redundant with the Big Five. We noticed very few
studies examining the relationship between GO and the Big Five
have used a three-dimensional GO measure, making us less con-
fident about the Big Five–PGO dimensions relationships. That
said, the pattern of relationships with personality variables imply
the GO dimensions are specific indicators of the motivational traits
achievement and anxiety (Kanfer & Heggestad, 1997). LGO rep-
resents approach motivation, whereas the PGO dimensions repre-
sent avoidant motivation.
Self-esteem was positively related to LGO and negatively re-
lated to PPGO and APGO. We theorized that these relationships
were likely to depend on the fulfillment of corresponding goals,
such that the GO dimensions would positively relate to self-esteem
to the extent that the individual attains corresponding (i.e., perfor-
mance vs. learning) goals. Future research is needed to test this
idea.
General self-efficacy was highly correlated with both the learn-
ing and avoid dimensions, which demonstrates the extent to which
Table 7
Incremental Validity of Trait GO on Job Performance
Variable BSEB⌬R
2
R
2
Step 1 .29
**
.29
Cognitive ability 0.51 0.06 .51
**
Openness to experience ⫺0.20 0.06 ⫺.20
**
Conscientiousness 0.22 0.06 .22
**
Extraversion 0.10 0.06 .10
Agreeableness 0.01 0.06 .01
Emotional stability ⫺0.06 0.06 ⫺.06
Step 2 .04
**
.33
Cognitive ability 0.54 0.05 .54
**
Openness to experience ⫺0.31 0.06 ⫺.31
**
Conscientiousness 0.12 0.07 .12
*
Extraversion 0.04 0.06 .04
Agreeableness 0.01 0.06 .01
Emotional stability ⫺0.04 0.06 ⫺.04
LGO 0.23 0.07 .23
**
PPGO 0.07 0.06 .07
APGO ⫺0.04 0.06 ⫺.04
Note. N ⫽260. LGO ⫽learning goal orientation; PPGO ⫽prove
performance goal orientation; APGO ⫽avoid performance goal orienta-
tion.
*
p⬍.05.
**
p⬍.01.
140 PAYNE, YOUNGCOURT, AND BEAUBIEN
LGO and general self-efficacy differ from one another (cf. Stevens
& Gist, 1997; Zweig & Webster, 2004). These findings suggest
that highly efficacious individuals are likely to have a strong LGO
and a weak APGO. Unfortunately, these results were based on a
relatively small number of studies. Given the magnitude of these
preliminary findings, we urge researchers to replicate these find-
ings with additional samples.
Proximal Consequences of GO
Consistent with our organizing framework, the GO dimensions
were more strongly related to self-regulatory constructs than the
performance constructs. However, contrary to popular belief, only
APGO was negatively related to these outcomes. Our results
support Brophy’s (2004) suspicion that it is really the APGO
dimension that is dysfunctional. PPGO was largely unrelated with
the outcomes examined in this study (cf. Elliot & Moller, 2003).
Our results indicate individuals who are highly efficacious for a
specific task tend to have a strong LGO and a weak APGO. A
number of researchers have noted the inconsistent pattern of rela-
tionships between the performance dimensions and specific self-
efficacy (e.g., Chen et al., 2000), suggesting the existence of
moderator variables. Our results suggest the presence of a moder-
ator for the LGO relationship. Specifically, there was substantially
more variability in the correlation between specific self-efficacy
and LGO than in the correlations between specific self-efficacy
and the performance dimensions. It also appears that the distinc-
tion between PPGO and APGO is extremely important in under-
standing the PGO–specific self-efficacy relationship, as only
APGO adversely affects specific self-efficacy.
Consistent with expectations, self-set goal level was positively
related to LGO, negatively related to APGO, and unrelated to
PPGO. In other words, individuals with high levels of LGO set
higher goals for themselves than do individuals with low levels of
LGO, and individuals with strong avoidance tendencies tend to set
low goals for themselves. The content of these goals, however, is
not always documented. Whereas the content is likely to be con-
gruent with one’s dominant GO, incongruency can occur and have
important implications for self-regulation, learning, and perfor-
mance (Kozlowski & Bell, 2006).
GO is frequently associated with self-regulatory constructs, such
as learning strategies, or cognitively oriented behaviors used to
influence learning. We found that both LGO and PPGO were
positively related to learning strategies. LGO had a large effect,
PPGO had a small effect, and APGO was virtually unrelated to
learning strategies. These findings indicate individuals with high
LGO and PPGO are more likely to engage in effective learning
strategies.
Some researchers have speculated distinctions among learning
strategies may be meaningful when examining relationships with
GO. For example, deep cognitive strategies such as paraphrasing
and summarizing have been associated with a high level of LGO,
whereas surface strategies have been associated with high levels of
the PGO dimensions (Elliot, McGregor, & Gable, 1999; Meece,
1994). However, we found the operationalization of learning strat-
egies was extremely inconsistent. Numerous learning strategy
measures have been used, and these distinctions have not always
been uniformly applied. Once the measurement of learning strat-
egies has been refined, further research examining GO–learning
strategy relationships will reveal the importance of the nature of
the learning strategy.
Consistent with VandeWalle and Cummings’s (1997) theoriz-
ing, individuals high in LGO were more inclined to seek feedback,
whereas individuals high in APGO were less inclined to do this.
Perhaps individuals with a strong PPGO will be more inclined to
seek feedback if they think they have performed well. Thus,
knowledge of expectations may moderate the PPGO–feedback-
seeking relationship.
5
It would also be interesting to examine
whether the type of feedback sought is influenced by GO (Butler,
1992, 1993; VandeWalle, 2003).
We examined feedback seeking as a consequence of GO, but
there are other feedback-related variables that may be associated
with GO. For example, GO has been associated with how one
interprets the purpose of feedback (Bobko & Colella, 1994; Farr et
al., 1993; Kanfer, 1990; VandeWalle, Cron, & Slocum, 2001). GO
has also been proposed as a moderator of the relationship between
feedback and the reactions one has to the feedback (Brett &
Atwater, 2001) as well as the relationship between initial and
subsequent goals (Cron, Slocum, & VandeWalle, 2001). GO might
also relate to the type and amount of feedback one gives to others
(Farr et al., 1993) and how individuals interpret (particularly
negative) feedback (Cron et al., 2001).
Individuals with high levels of APGO and PPGO were likely to
have high levels of state anxiety, whereas high levels of LGO were
associated with lower levels of state anxiety. This appears to be
true regardless of whether one has experienced previous failure,
suggesting prior failure is not a necessary condition for these
relationships. However, longitudinal studies contrasting conditions
with and without prior failure are needed to confirm this prelim-
inary interpretation. Additionally, state anxiety was typically op-
erationalized with a unidimensional measure of test anxiety; yet
Elliot and McGregor (1999) found the worry component of test
anxiety to be more relevant to GO than the emotionality compo-
nent.
Distal Consequences of GO
Early GO studies indicated children with learning goals tended
to outperform children with performance goals (e.g., Farrell &
Dweck, 1985). Our results reveal that LGO has a small equally
positive correlation with learning and academic performance, and
APGO has a small negative relationship with learning. Further,
state LGO has an even stronger positive relationship with learning,
suggesting it is an even better predictor of learning than trait LGO.
These findings indicate individuals with a high trait and state LGO
and a low APGO are likely to learn more. These results further
indicate that it is APGO, not PPGO, that is detrimentally related to
learning. Contrary to the early belief that PPGO leads to negative
outcomes (Brophy, 2004; DeShon & Gillespie, 2005), our research
shows that it has virtually no relationship with learning or aca-
demic performance.
High levels of trait and state LGO appear to also be advanta-
geous to task and job performance, and a high level of trait and/or
state PPGO may also be beneficial. A high level of trait APGO was
related to lower task and job performance levels. Across all four
5
We thank an anonymous review for suggesting this possibility.
141
GOAL ORIENTATION META-ANALYSIS
outcomes, a high trait and state LGO and a low trait APGO were
related most favorably, and high trait and state PPGO did not relate
adversely to performance. Given the negative relationship between
APGO and state anxiety, it would be interesting to see whether the
negative relationships between APGO and the performance out-
comes are reduced when state anxiety is controlled.
On the other hand, these bivariate relationships do not reveal
potentially more interesting and complex relationships that may
exist between GO and various outcomes. For example, Dweck
(1986) described the relationship between PGO and performance
as dependent on “confidence in present ability” (p. 1041), which
GO researchers often overlook (see K. G. Brown, 2001, for an
exception). Additionally, early research demonstrated PGO was
related to decrements in performance after initial failure (Diener &
Dweck, 1978, 1980), which in turn is related to self-efficacy. This
is another layer of complexity in the PGO–performance relation-
ship often overlooked. Thus, one possible reason no relationships
were found between LGO or PPGO and task performance is
because they are moderated by previous failure, confidence, or
both.
LGO predicted job performance above and beyond cognitive
ability and the Big Five. This finding suggests LGO may be a
valuable predictor of job performance. Additional research is
needed to determine whether this is true for all jobs. It seems it
would be most valuable for jobs requiring employees to embrace
new learning opportunities and adapt to change.
Limitations
Generalizations of our findings should take into consideration
our inclusion criteria (e.g., adult samples, trait and state measures
of GO). However, given the large number of studies included in
several of the analyses (e.g., cognitive ability, task-specific self-
efficacy, learning, and academic performance), we feel quite con-
fident about some of our results.
We organized the variables into three categories: antecedents,
proximal consequences, and distal consequences. When available,
we used theory to place variables into their respective categories.
However, meta-analyses do not permit drawing inferences about
causality or reveal reciprocal relationships. Clearly, longitudinal
studies that establish temporal precedence, contiguity, and con-
stant conjunction (Cook & Campbell, 1979) are needed to better
test the extent to which the proposed antecedents and conse-
quences have been labeled appropriately.
It is unclear how much common method variance is contributing
to relationships particularly with the proposed antecedents because
most of these were also self-reported. Also, the relationships
between the variables we examined are likely to be much more
complex than how they are depicted in Figure 1 (e.g., we do not
include feedback loops). Finally, the variables we examined were
limited to those in which there were a sufficient number of studies
to meaningfully aggregate. Similarly, we were only able to con-
duct the incremental validity analyses for job performance, be-
cause we were unable to locate all of the necessary rho values for
the other distal consequences. Thus, the use of a meta-correlation
matrix also has some limitations (Viswesvaran & Ones, 1995).
Directions for Future Research
Although we examined a large number of variables, there are
other antecedents and consequences of GO that merit additional
study. For example, beliefs about the causes of success (Duda &
Nicholls, 1992), locus of control (Dweck & Leggett, 1988), fear of
failure (Elliot & Church, 1997), the presence of off-task cognitions
(Kanfer & Ackerman, 2000), and trait anxiety may be antecedents
of GO. Perfectionism (McGregor & Elliot, 2002), procrastination
(McGregor & Elliot, 2002), and positive affect (Jagacinski &
Nicholls, 1984) may be outcomes of GO.
Meta-analytic results do not explain why relationships exist.
Additional research is needed to identify the mechanisms facili-
tating the relationships identified (e.g., Elliot et al., 1999). For
example, why is APGO negatively related to learning and task
performance? One assumed explanation is that individuals with
high levels of APGO experience intrusive thoughts while perform-
ing; however, this theory has not been extensively tested. Alter-
natively, or in addition, test anxiety has been proposed as a
mediator of the APGO–performance relationship (Elliot & McGre-
gor, 1999). Further, the extent to which self-regulatory constructs
and processes mediate GO– distal outcome relationships needs to
be tested. There may also be other mediators such as persistence or
planning. The distal outcomes also vary in proximity to GO and
could be modeled further (e.g., LGO relates to learning, which in
turn relates to academic performance).
With additional rho values, the incremental validity of disposi-
tional GO could be further examined for learning, academic per-
formance, and task performance in the lab. Also, self-regulatory
processes like goal setting (cf. Seijts, Latham, Tasa, & Latham,
2004) could be added to these analyses as control variables.
Finally, the incremental validity of state GO above and beyond
trait GO remains to be tested (cf. Dragoni, 2005).
Our research further illuminates the value of the distinction
between the prove–approach and avoid conceptualizations of per-
formance goals. APGO was negatively related to many more
self-regulatory constructs and outcomes than PPGO. Thus, the
bifurcation of LGO into approach and avoid components may also
prove both theoretically and empirically meaningful.
Potential Moderators
We attempted to assess a number of theoretical and exploratory
moderators in our study; however, the nature of the data available
often prevented a true examination of these variables or the find-
ings did not warrant journal space. For example, Nicholls’s (1984)
theory is largely focused on internal and external referents and the
opportunity to compare one’s performance with others. Thus,
Rawsthorne and Elliot (1999) proposed the presence of others
during task performance could make a situation feel “evaluative”
because others are present to judge one’s performance. The pres-
ence of others is likely to strengthen relationships between the
PGO dimensions and performance and/or state anxiety, because
both dimensions of PGO are associated with an external referent.
The presence of others also provides a source of normative infor-
mation (Utman, 1997) and could facilitate cognitive interference
(Rawsthorne & Elliot, 1999). Indirect evidence for this argument
was observed by Utman (1997), who noted that the effect of
experimentally induced learning goals over performance goals was
142 PAYNE, YOUNGCOURT, AND BEAUBIEN
stronger in group settings than when performance was measured
alone. We were unable to test the presence of others as a moder-
ator, because most primary studies do not provide sufficient in-
formation about the context in which participants performed. Al-
though many studies indicated whether performance took place in
a solitary or group context, the extent to which the other people
were privy to the participants’ performance and vice versa was
rarely clear. Thus, we urge GO researchers to make contextual
information more clear so this moderator and Nicholls’s (1984)
theory can be further tested.
Other potential moderators of the GO–performance relation-
ships are task characteristics like task complexity, time on task,
and task demands (e.g., task difficulty and task consistency; see
Steele-Johnson et al., 2000). Utman (1997) found the comparative
advantage of learning goals over performance goals was more
pronounced for complex tasks. Unfortunately, details about task
characteristics are not typically included in the primary studies or
there is minimal variability on these characteristics across studies
(e.g., task complexity of college exams). Future research might
also explore moderators of the relationships between GO and its
antecedents.
Although we do not provide the detailed results here, we also
examined the measures used to assess GO as a potential moderator.
In particular, we examined three frequently used measures of GO:
Button et al. (1996), Elliot and his colleagues (Elliot & Church,
1997; Elliot & McGregor, 2001), and VandeWalle (1996, 1997).
Whereas there were no substantial differences across the measures,
examining the GO measure indirectly tests the influence of the
idiosyncrasies of the various measures. For example, by compar-
ing correlations with Button et al.’s PGO measure with Vande-
Walle’s (1996, 1997) and Elliot’s (Elliot & Church, 1997; Elliot &
McGregor, 2001) PPGO and APGO measures, we could see how
appropriate it was for us to code unidimensional PGO scales as
PPGO. The primary difference we observed with Button et al.’s
PGO measure was that it had no relationship with learning strat-
egies. Thus, the inclusion of unidimensional PGO measures in
PPGO coding may have attenuated the positive relationship be-
tween PPGO and learning strategies.
Comparing GO measures also allows for an indirect test of the
extent to which GO is domain specific, as the various measures
vary in specificity (DeShon & Gillespie, 2005). For example,
VandeWalle (1997) advocated using items at a midlevel of spec-
ificity (i.e., for major life domains: academics, work, and athletics)
and has two measures of GO (one for work and one for academics:
VandeWalle, 1996, 1997). Similarly, Elliot’s (Elliot & Church,
1997; Elliot & McGregor, 2001) items are more academically
oriented, referring to performance in “this class.” Button et al.’s
measure appears to be much broader. The primary difference
observed for VandeWalle’s (1996, 1997) scales was that his LGO
measure produced relatively stronger relationships with self-set
goal level, feedback seeking, and task performance.
Finally, examining the GO measure as a potential moderator
also reveals the extent to which the inclusion of internal versus
external referents (others) and normative information in the items
influences relationships with the PGO dimensions. For example,
Elliot’s (Elliot & Church, 1997; Elliot & McGregor, 2001) APGO
measure includes explicitly normative items. We observed a num-
ber of trends regarding Elliot and colleagues’ measure (Elliot &
Church, 1997; Elliot & McGregor, 2001). First, in general, Elliot’s
(Elliot & Church, 1997; Elliot & McGregor, 2001) scales produced
stronger relationships than Button et al.’s (1996) or VandeWalle’s
(1996, 1997) measures with a number of variables (e.g., academic
performance). Second, Elliot’s (Elliot & Church, 1997; Elliot &
McGregor, 2001) LGO and PPGO correlated slightly more posi-
tively with one another. Third, Elliot’s (Elliot & Church, 1997;
Elliot & McGregor, 2001) APGO generated the strongest negative
relationships with cognitive ability, specific self-efficacy, and
learning strategies compared with the other measures. However,
caution is warranted, as many of these results are based only on
two studies. Fourth, Elliot’s (Elliot & Church, 1997; Elliot &
McGregor, 2001) LGO produced the strongest relationship with
specific self-efficacy and the weakest relationship with conscien-
tiousness and learning strategies. Finally, Elliot’s (Elliot &
Church, 1997; Elliot & McGregor, 2001) PPGO generated the
largest correlation with conscientiousness and a positive relation-
ship with specific self-efficacy.
Given Kanfer and Ackerman’s (2000) recent call for research on
how demographic variables influence motivational processes and
work outcomes and given propositions about the influence of age
on work motivation (Kanfer & Ackerman, 2004), we also exam-
ined the relationships between GO and sex as well as age. Al-
though results are not reported here, we found no substantial
bivariate relationships between trait GO and sex and age. That
said, it might be fruitful to examine interactions between GO
dimensions and these demographic characteristics, as both sex and
age have been proposed as moderators of the GO–performance
relationship (Midgley et al., 2001). The relationship between GO
and ethnicity might also be explored as more cross-cultural GO
research is conducted (e.g., Gong & Fan, 2006; Gully, Phillips, &
Tarique, 2003; Lee, Tinsley, & Bobko, 2003).
In summary, we examined how trait and state GO relate to a
wide range of variables further defining the nomological network
for the GO dimensions: LGO, PPGO, and APGO. Whereas this
construct was initially examined in the developmental, educa-
tional, and school psychology literature, it appears also to play an
important role in the workplace. Our research confirms meaningful
and differential relationships between three dimensions of GO and
various outcomes including job performance.
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Appendix
Artifact Distribution
Received September 12, 2004
Revision received January 30, 2006
Accepted February 8, 2006 䡲
Variable kMinimum Maximum Mdn SD M
TLGO 173 .48 .94 .81 .06 .81
TPPGO 166 .54 .97 .79 .08 .79
TAPGO 59 .64 .88 .78 .06 .78
SLGO 18 .61 .91 .80 .09 .79
SPPGO 17 .66 .95 .78 .07 .78
SAPGO 3 .73 .90 .81 .09 .81
Note. TLGO ⫽trait learning goal orientation; TPPGO ⫽trait prove
performance goal orientation; TAPGO ⫽trait avoid performance goal
orientation; SLGO ⫽state learning goal orientation; SPPGO ⫽state prove
performance goal orientation; SAPGO ⫽state avoid performance goal
orientation.
150 PAYNE, YOUNGCOURT, AND BEAUBIEN
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