Journal of Applied Psychology
1998, Vol. 83, No. 4, 654-665
Copyright 1998 by the American Psychological Association, Inc.
0021-9010/98/$3.00
Conscientiousness, Goal Orientation, and Motivation to Learn During
the Learning Process" A Longitudinal Study
Jason A. Colquitt and Marcia J. Simmering
Michigan State University
Conscientiousness and goal orientation were examined as (a) predictors of motivation
to learn and (b) moderators of reactions to performance levels during the learning process,
using an Expectancy × Valence framework. Learners (N = 103) participated in a 6week course in which an objective performance goal was assigned. Results indicated that
conscientiousness and learning orientation were positively related to motivation to learn
both initially and after performance feedback was given, whereas performance orientation
was negatively related to motivation to learn at those 2 time periods. In addition, learning
and performance orientation moderated the relationships between performance levels
during the learning process and subsequent expectancy and valence.
levels throughout the learning process, particularly in the
wake of early difficulty (Fedor, 1991; Phillips & Gully,
1997). This issue is critical because many learners struggle during the early phases of the training program and
often fall short of the objective performance goals that
are typically used (Farr, Hofmann, & Ringenbach, 1993).
Our study addressed both of these gaps by integrating
two personality variables, conscientiousness and goal orientation, with motivation to learn to assess their role in
learning. We examined these issues in the context of a 6week academic course, which provided an opportunity to
study the learning process using a more comprehensive,
longitudinal approach than many past investigations (e.g.,
Baldwin, Magjuka, & Loher, 1991; Martocchio & Webster, 1992; Phillips & Gully, 1997; Quinones, 1995). We
gave a difficult but achievable performance goal to each
learner as part of the course, matching the type of goal
normally assigned to employees in training programs and
organizational settings (Farr et al., 1993; Locke & Latham, 1990). We then examined personality relationships
at two time periods during the learning process: the beginning of the course and directly after learners were given
feedback relating their current performance levels with
their assigned goal (approximately halfway through the
course). Gagne and Medsker (1996) noted that these two
time periods are critical junctures in the learning process.
We cast conscientiousness and goal orientation as distal
variables that influenced learning through the more proximal mechanism of motivation to learn. Kanfer (1991)
advocated using this distal-proximal framework for examining personality effects because it places those effects
in a more theoretical context (see also Judge & Martocchio, 1995). We predicted that feedback regarding one's
performance levels vis-?a-vis the assigned goal would alter
subsequent motivation levels. In addition, we examined
The increasing complexity of the tasks to be performed
in the future workplace is likely to place an even greater
emphasis on training programs, as evidenced by the fact
that no less than six chapters in Howard's (1995) The
Changing Nature of Work noted the need for improved
training approaches. The increased emphasis on training
will necessitate a commitment on the part of selection
programs to provide motivated trainees. Such a commitment could create internal fit and synergy among human
resources practices, potentially improving firm performance (Becker & Gerhart, 1996; Delery & Doty, 1996).
One stream of literature that can inform practitioners in
this area is the research on motivation to learn, defined
here as a desire on the part of trainees to learn the content
of the training program (Noe, 1986).
Research has consistently shown a positive relationship
between motivation to learn and learning across a variety
of settings, but several important gaps remain. For example, research linking dispositional personality variables to
motivation to learn has been limited and often atheoretical.
In addition, researchers have not adequately explored
what types of learners continue to show high motivation
Jason A. Colquitt and Marcia J. Simmering, Eli Broad Graduate School of Management, Michigan State University.
We wish to thank John R. Hollenbeck, Raymond A. Noe,
Linn Van Dyne, and J. Kevin Ford for their helpful comments
and suggestions on an earlier draft of this article. An earlier
version of this article was presented at the 1997 meeting of the
Academy of Management, Boston, Massachusetts.
Correspondence concerning this article should be addressed
to Jason A. Colquitt, Eli Broad Graduate School of Management,
N475 North Business Complex, Michigan State University, East
Lansing, Michigan 48824. Electronic mail may be sent to
colquitt @pilot.msu.edu.
654
RESEARCH REPORTS
655
PERSONALITYVARIABLES
Conscientiousness
LearningOrientation
PerformanceOrientation
I
ExV
INITIAL
MOTIVATION
TO LEARN
-('-~ Pre-Feedback
w
Learning
ExV
ExV
MOTIVATION
TO LEARN
~
PoLset-Fsem
edn
ibgack
PERFORMANCE
FEEDBACK
DELIVERED
I
3 WEEKS
tI
3 WEEKS
Figure 1. Conceptual model illustrating the relationships among personality variables, motivation to learn, and learning. E x V = Expectancy x Valence variables.
the degree to which learners' conscientiousness and goal
orientation levels moderated these effects, which are summarized in our conceptual model presented in Figure 1.
The Relationship B e t w e e n Conscientiousness, Goal
Orientation, and Motivation to L e a r n
Noe (1986) presented a conceptual model detailing
antecedents and outcomes associated with trainee motivation to learn. The primary antecedents in Noe's model
were job attitudes, reactions to feedback, contextual factors, and individual perceptions of self-efficacy or expectancy (i.e., effort-performance contingencies). 1 In the
decade since then, other researchers have argued that motivation to learn should be examined from both an expectancy and a valence perspective, consistent with Vroom's
(1964) expectancy theory (Baldwin & Karl, 1987; Mathieu, Tannenbaum, & Salas, 1992). This perspective asserted that trainees can desire to learn the training content
(i.e., have high levels of motivation to learn) for two
reasons: (a) They see a relationship between their effort
and actual learning progress (i.e., expectancy), and (b)
the outcomes that can be attained from such progress are
valued (i.e., valence).
Unfortunately, motivation to learn research has largely
ignored this approach, particularly in terms of valence
(e.g., Noe & Schmitt, 1986). Furthermore, even when
Expectancy x Valence approaches have been used, only
correlations with an overall summed index have been reported (Baldwin & Karl, 1987; Mathieu et al., 1992). As
a result, studies that yielded correlations between motivation to learn and attitudinal predictors (i.e., job involvement or organizational commitment) or situational predictors (e.g., the provision of choice, the framing of the
training assignment, or the presence of situational constraints) were less able to empirically show why the corre-
lation was there (Baldwin et al., 1991; Hicks & Klimoski,
1987; Martocchio & Webster, 1992; Mathieu et al., 1992;
Noe & Schmitt, 1986; Quinones, 1995; Ryman &
Biersner, 1975; Tannenbaum, Mathieu, Salas, & CannonBowers, 1991). An explicit treatment of relevant mediators, lacking in the extant literature, can help answer the
" w h y " questions that are a central component of good
theory (Whetten, 1989).
Personality variables have also been overlooked in this
research, except for locus of control, which has failed to
yield significant correlations, particularly when ability is
controlled for (Lied & Pritchard, 1976; Noe & Schmitt,
1986). Conspicuous by their absence are conscientiousness, which "may be the most important trait-motivation
variable in the work domain" (Barrick, Mount, & Strauss,
1993, p. 721 ), and goal orientation, a variable that holds
untapped promise for training applications (Button, Mathieu, & Zajac, 1996; Farr et al., 1993). An Expectancy
X Valence approach would be helpful in linking these
personality variables to motivation to learn, as evidenced
by similar efforts in the goal commitment literature (Gellatly, 1996; Hollenbeck & Klein, 1987; Hollenbeck, Williams, & Klein, 1989). Goal commitment researchers have
1Most researchers distinguish between self-efficacy (e.g.,
Bandura & Cervone, 1986) and expectancy (Vroom, 1964).
Gist and Mitchell (1992) noted that expectancy is a probabilistic
judgment regarding effort-performance contingencies that is
driven by self-efficacy, a person' s estimate of his or her "capacity to orchestrate performance on a specific task" (p. 183).
However, it is difficult to separate the two in terms of relationships to motivation to learn, possibly explaining why they were
integrated in Noe's model (e.g., Noe & Schmitt, 1986). Similarly, our study discusses both expectancy and self-efficacy as
examples of a larger expectancy component of motivational
theories (e.g., Kanfer, 1991).
656
RESEARCH REPORTS
used Expectancy × Valence approaches for personality
effects, thereby placing those effects in a more theoretical
context and responding to past criticisms of personality
research (Davis-Blake & Pfeffer, 1989; Judge & Martocchio, 1995; Kanfer, 1991). Thus, in our study, we examine
how conscientiousness and goal orientation relate to motivation to learn through the mechanisms of expectancy
and valence, as shown in Figure 1.2
search, Gellatly (1996) failed to demonstrate such a relationship. Thus, although existing research is equivocal,
we propose the following:
Hypothesis 1. Conscientiousness will be positively related
to motivation to learn, both initially and after performance
feedback is given, an effect that will be mediated by expectancy and valence.
Goal Orientation
Conscientiousness
Conscientiousness, a trait reflecting qualities such as
being reliable, hardworking, self-disciplined, and persevering (McCrae & Costa, 1987), has been linked to a
variety of positive work outcomes (Barrick & Mount,
1991). Meta-analytic evidence found conscientiousness to
be a predictor of job performance across many occupational groups, and other research has linked it to goal
commitment and self-set goal setting (Barrick & Mount,
1991; Barrick et al., 1993; Gellatly, 1996). It is surprising
that no published research has used conscientiousness as a
predictor of motivation to learn. However, recent research
suggests a link between conscientiousness and expectancy. Martocchio and Judge (1997) demonstrated a significant relationship between conscientiousness and selfefficacy in a computer software training setting. In a
similar manner, Gellatly linked conscientiousness and expectancy using an arithmetic task.
These results can be explained theoretically by examining the antecedents of self-efficacy reviewed by Gist and
Mitchell (1992). For example, the authors suggested that
self-efficacy is driven in part by one's assessment of personal resources and constraints. In reflecting on their personal resources, conscientious individuals are likely to
perceive that they are diligent and hardworking
(McCrae & Costa, 1987) and should consider that a favorable resource. Another antecedent of self-efficacy is an
individual's analysis of task requirements. Conscientious
individuals are more likely to accurately assess task requirements because of their tendency to be organized and
systematic (McCrae & Costa, 1987) and should have
more confidence in their ability to meet those requirements given their traditionally high performance levels
(Barrick & Mount, 1991).
There are also reasons to expect a positive relationship
between conscientiousness and valence given that conscientiousness subsumes the more specific dimension of need
for achievement. Hollenbeck and Klein (1987) predicted a
link between need for achievement and goal commitment
because individuals with high levels of need for achievement are more likely to value high performance. Similarly,
conscientious individuals are more likely to set challenging goals for themselves and stay committed to those goals
(Barrick et al., 1993). However, although a conscientiousness-valence linkage would be consistent with this re-
Goal orientation is a relativelystable dispositional variable that assumes two forms: (a) a learning orientation
in which increasing competence by developing new skills
is the focus and (b) a performance orientation in which
demonstrating competence by meeting normative-based
standards is deemed critical. The construct was originally
derived from Dweck' s work with children in the education
domain (e.g., Dweck, 1986, 1989). Her research detailed
adaptive and maladaptive behavioral patterns that arose
on the basis of children's beliefs about ability (Dweck,
Hong, & Chiu, 1993). Entity theorists believed that ability
levels were fixed, whereas incremental theorists believed
that ability levels were malleable and could be increased.
These implicit theories affect the kinds of goals individuals choose to pursue, with entity theorists holding strong
performance goals and weak learning goals and incremental theorists showing the opposite tendency.
Much of the goal orientation research in the education
literature has used child samples (e.g., Elliott & Dweck,
1988; Seifert, 1995; Stipek & Gralinski, 1996). The extent
to which the findings of these studies generalize to adults
is still at issue, but, in general, adult samples have shown
convergent results (e.g., Archer, 1994; Bouffard, Boisvert,
Vezeau, & Larouche, 1995; Duda & Nicholls, 1992; Elliot & Harackiewicz, 1994; Greene & Miller, 1996; Harackiewicz & Elliot, 1993; Schraw, Horn, ThorndikeChrist, & Bruning, 1995). Only recently has goal orientation become a focus of organizational researchers, but
most results have supported previous research (e.g., Button et al., 1996; Fisher & Ford, in press; Hofmann, 1993;
Phillips & Gully, 1997).
Button et al. noted that goal orientation holds great
2 Vroom's (1964) conceptualization of valence differentiated
the valence of the first-order outcome, performance, and secondorder outcomes tied to performance (e.g., pay or promotions).
In an academic setting, the distinction between first- and secondorder outcomes is less clear, given that grades are outcomes
received because of performance and are direct measures of
performance itself. Thus, we referenced valence toward getting
a good grade, doing well in the course, and achieving success
in the class. Vroom also posited that valence and expectancy
are independent, a stance that contrasted with Atkinson and
Birch's (1964) theory of achievement motivation that suggested
that more difficult tasks have higher valences.
RESEARCH REPORTS
promise for organizational applications and provided several recommendations for future research. First, they provided factor analytic evidence suggesting that goal orientation is best conceptualized as two independent dimensions (learning and performance orientation) rather than
a unidimensional continuum. That is, individuals can be
simultaneously high or low on both learning and performance orientations, contrary to the conceptualization of
Dweck and her colleagues (e.g., Dweck, 1986, 1989).
The extent to which past findings in this area can be
generalized to the two-dimensional case is as yet unknown, although early research is consistent with
Dweck's predictions (Button et al., 1996; Phillips &
Gully, 1997). Finally, Button et al. further argued that
individuals have dispositional goal orientations that predispose them to react to situations in specific ways but
that situational cues can impact those predispositions.
Thus, the construct has both trait and state qualities, suggesting that test-retest reliabilities may be lower than
those of other trait measures such as conscientiousness
(Button et al., 1996; DeShon & Fisher, 1996).
Because of the implicit theories from which they are
derived, there are reasons to expect relationships between
both goal orientations and expectancy. The higher one's
performance orientation, the more one should react to
difficult tasks with doubts about ability levels (e.g., E1liott & Dweck, 1988). This relationship would explain
the finding that highly performance-oriented individuals
often avoid difficult tasks in favor of more achievable
ones (Dweck & Leggett, 1988). In contrast, the stronger
one's learning orientation, the more one should continue
to believe that ability and skill can be increased to achieve
success (e.g., Dweck, 1986, 1989). This connection suggests a positive relationship between learning orientation
and expectancy and a negative relationship between performance orientation and expectancy. Indeed, Phillips and
Gully (1997) recently demonstrated both of these predicted relationships in the context of an undergraduate
course.
There are also reasons to expect relationships between
both goal orientations and valence. Highly learning-oriented individuals are more likely to seek challenges and
view high performance as indicative of increased mastery
(Dweck & Leggett, 1988). One would therefore expect
that the more learning oriented a person is, the more he
or she would place value on high performance levels.
In contrast, Elliott and Dweck (1988) noted that, when
performance orientations are highlighted, individuals
avoid difficult tasks in favor of moderate ones (when
perceived ability is high) or easy ones (when perceived
ability is low). This finding suggests that, because the
learners in our sample were given difficult goals, performance orientation should be negatively related to valence.
Performance-oriented individuals also respond to difficult
tasks with negative affect (Dweck & Leggett, 1988; El-
657
liott & Dweck, 1988), which would also suggest a negative relationship with valence given that valence is based
on anticipated satisfaction. Therefore,
Hypothesis 2. Learning orientation will be positively related to motivation to learn, both initially and after performance feedback is given, an effect that will be mediated
by expectancy and valence.
Hypothesis 3. Performance orientation will be negatively
related to motivation to learn, both initially and after performance feedback is given, an effect that will be mediated
by expectancy and valence.
Trait Variables as Moderators of Reactions to
P e r f o r m a n c e Levels
Not all learners succeed in every facet of the learning
process--many experience some difficulty in learning the
material (Farr et al., 1993). Whether learners internally
perceive this difficulty or receive feedback regarding it,
reactions to performance levels are likely to have important motivational consequences (Ilgen, Fisher, & Taylor, 1979; Kluger & DeNisi, 1996; Phillips, Hollenbeck, &
Ilgen, 1996; Podsakoff & Farh, 1989; Taylor, Fisher, &
Ilgen, 1984). It is important to understand these consequences to maximize the effectiveness of goal-directed
learning settings. In general, researchers have shown that
low performance levels decrease expectancy and self-efficacy levels (e.g., Bandura & Cervone, 1986; Gist &
Mitchell, 1992). Relationships with valence may also be
negative, but they depend on complex cognitive and selfreactive influences such as self-satisfaction, attribution,
and referent comparison (Bandura, 1991).
In our study, we gave learners feedback halfway
through the course that related their current performance
to their assigned goal. Gagne and Medsker (1996) emphasized the importance of giving feedback during the learning process, identifying it as an instructional event that
facilitates learning. We were interested in learners' motivation in the wake of this feedback, in particular how
those effects differed according to learners' levels of conscientiousness and goal orientation. Fedor (1991) noted
that empirical research addressing the role played by personality in responding to performance feedback is needed.
Conscientiousness
Martocchio and Judge (1997) noted that conscientious
individuals have a tendency to engage in self-deception,
a notion also supported by Barrick and Mount (1996).
That is, conscientious individuals are more likely to believe that they are doing better than they truly are. This
self-deception should prevent large decreases in expectancy perceptions because such individuals may refuse to
accept the fact that poor performance levels are indicative
of true ability. Indeed, self-deceivers often reach high
658
RESEARCH REPORTS
achievement levels for this reason (Martocchio & Judge,
1997). Also, conscientious individuals, who are by definition persevering and disciplined (McCrae & Costa, 1987),
should refuse to give up in the face of difficulty. Indeed,
they should continue to hold high perceptions of valence
given their tendency to commit to assigned goals (Barrick
et al., 1993). Thus, we predict the pattern shown in Figure
2 and posit the following:
Hypothesis 4. The relationship between performance levels
at the time feedback is given and subsequent (a) expectancy
and (b) valence will be moderated by conscientiousness,
such that lower performance will be less associated with
lower expectancy or valence for highly conscientious
learners.
Goal Orientation
Goal orientation is often discussed in the context of
responses to feedback regarding performance (e.g.,
Dweck, 1986, 1989; Farr et al., 1993). Research has suggested that learning-oriented individuals should be less
likely to respond to poor progress during learning with
lowered expectancy. Button et al. (1996) demonstrated
that the more learning oriented individuals are, the more
they subscribe to the incremental theorists' position that
ability levels can be increased to correct poor progress.
Such beliefs should buffer these individuals against large
decreases in expectancy (Bobko & Colella, 1994). Moreover, individuals with strong learning orientations are
more likely to engage in problem solving and altering of
strategies when they encounter low performance levels
(Dweck & Leggett, 1988; Elliott & Dweck, 1988). In
contrast, the more performance oriented people are, the
more they believe the entity theorists' position that ability
is fixed (Button et al., 1996). Thus, these individuals
should meet difficulty with marked decreases in expec-
tancy as they shift their focus to ability inadequacies and
off-task thoughts (Bobko & Colella, 1994).
In terms of valence, research has shown that learningoriented individuals react to challenges with positive affect, pride, and intrinsic motivation (Dweck & Leggett,
1988). The more challenging a task becomes, the more it
is perceived as an opportunity to build competence. This
relationship suggests that highly learning-oriented individuals should be buffered against the tendency to associate poor performance with lower valence. Indeed, such
individuals may find themselves immersed in the task because of increased cognitive engagement (Duda & Nicholls, 1992; Greene & Miller, 1996). Performance-oriented individuals, on the other hand, traditionally do not
react to poor progress in a constructive manner. Indeed,
Fisher and Ford (in press) showed a link between performance orientation and off-task attention, and Dweck and
Leggett (1988) related performance orientation to anxiety
and negative affect. Such results suggest that these learners lack self-leadership skills (Manz, 1986) because they
focus on unpleasant aspects of the task (e.g., pressure or
uncertainty) rather than pleasant ones (e.g., challenge or
learning). Thus, the more difficulty one experiences, the
more the desire to perform well should decline. We therefore predict the pattern shown in Figure 2 and posit the
following:
Hypothesis 5. The relationship between performance at the
time feedback is given and subsequent (a) expectancy and
(b) valence will be moderated by learning orientation, such
that lower performance will be less associated with lower
expectancy or valence for highly learning-oriented learners.
Hypothesis 6. The relationship between performance at the
time feedback is given and subsequent (a) expectancy and
(b) valence will be moderated by performance orientation,
such that lower performance will be less associated with
lower expectancy or valence for less performance-oriented
learners.
The Relationship B e t w e e n Motivation
to L e a r n and Learning
-7
-6
-O
High Conscientiousness
High Learning Orientation
Low Performance Orientation
-4
-3
Low Conscientiousness
Low Learning Orientation
-2
13-
High Performance Orientation
-1
I
I
HIGH
LOW
Pre-Feedback Learning Levels
Figure 2. Predicted patterns of Personality × Learning interactions for subsequent expectancy and valence.
Linking trait variables to motivation to learn is only
valuable to the degree that motivation levels are connected
to actual learning differences. Fortunately, the relationship
between motivation to learn and learning has been robust,
predicting both cognitive and skill-based outcomes (e.g.,
Baldwin et al., 1991; Martocchio & Webster, 1992; Mathieu et al., 1992; Noe & Schmitt, 1986; Quinones, 1995;
Tannenbaum et al., 1991). On the basis of this research,
Hypothesis 7. Initial motivation to learn will be positively
related to prefeedback learning; postfeedback motivation
to learn will be positively related to postfeedback learning.
Method
Sample
The participants were 103 undergraduates enrolled in two
sections of a management course. The 6-week course was the
RESEARCH REPORTS
learning program, with performance at the midpoint of the
course (i.e., when performance feedback was given) and performance during the remainder of the course serving as the learning
measures. This paradigm has been used successfully by researchers in the areas of motivation to learn and goal setting
(Hollenbeck, Williams, & Klein, 1989; Mathieu et al., 1992;
Phillips & Gully, 1997). Hollenbeck et al. (1989) examined
goal commitment in an undergraduate management course, Phillips and Gully examined self-set goal setting in a similar setting,
and Mathieu et al. examined motivation to learn in an undergraduate bowling course. Participation was voluntary, and we
awarded extra credit points (6% of the total points) for involvement. All students participated.
Procedure
The authors served as instructors for the classes included in
the sample. Each author served as experimenter for the other
author's class; thus, no author collected data, had access to data,
or even discussed the study with his or her own students. We
asked students to participate in a term-long study involving
personality variables, and we informed them that they would
be asked to provide some academic information and to complete
several surveys. We gave the students feedback on the correlates
of various personality variables and told them how such information could be used in the workplace during the final week
of the course.
Given that reactions to learning levels were to occur as a
result of performance feedback, we had to create an objective
achievement standard, which we did by assigning each student a
goal for class performance. We collected the current cumulative
grade point average (GPA) for each participant and used it to
create a grade goal on the basis of the method used by Hollenbeck, Williams, & Klein (1989). Specifically, we added .25
points to each participant's GPA, and the resulting GPA was
then expressed as a percentage of total points on the basis of
the course's grading scale. For example, a student with a cumulative GPA of 3.25 was given a GPA goal of 3.5, which was
expressed as a grade goal of 86%. 3Thus, these goals represented
a difficult but achievable goal for student learning (Hollenbeck
et al., 1989). We distributed grade goals to the students during
the second class period in sealed envelopes marked with identification numbers. Each participant received the following explanation with the grade goal:
659
After receiving the grade goal, each student completed the
first of two in-class surveys measuring expectancy, valence, and
motivation to learn. We measured goal orientation and goal
commitment (used as a control variable) concomitantly, and we
also assessed goal orientation at the conclusion of the term.
Note that we examined goal orientation as a dispositional trait,
one that, like conscientiousness, could be used in selection batteries. However, goal orientation has been shown to have situational variability (Button et al., 1996; Dweck, 1975). Assessing
goal orientation three separate times allowed us to account for
this variance source. Specifically, we measured dispositional
goal orientation by averaging the initial, midpoint, and conclusion scores. This procedure provided us with one disposition
score because it collapsed across the contaminating influence
of situational goal orientation variance, which was outside the
scope of our study. The average correlation among the three
learning orientation scores was .62; the average correlation
among the three performance orientation scores was .60. This
level is consistent with other uses of Button et al.'s measure
(DeShon & Fisher, 1996).
The first course examination was given 2 weeks (four class
periods) after students received their grade goals. We recorded
performance on this examination in percentage terms, and participants received performance feedback during the class period
immediately following the exam. This feedback read as follows:
"Your grade goal at the beginning of the semester was a
%.
Your current grade in the course is a
%. That means you
are currently
% (above/below) your grade goal." During
the subsequent class meeting, students completed the second inclass survey, which again assessed expectancy, valence, motivation to learn, goal orientation, and goal commitment.
We gave the final examination for the course 3 weeks (five
class periods) after the administration of the grade goal feedback, and we used it to assess learning at the conclusion of the
learning process. We gave the final goal orientation measure at
the beginning of this class period. We also gave participants a
separate out-of-class survey that assessed conscientiousness and
asked them to complete the scale on their own time. We collected
all scales at least 2 weeks before the end of the course.
Measures
Your goal for a grade in this course is: _ _ %
Motivation to learn. We used three items adapted from Noe
and Schmitt (:1986) to measure motivation to learn. One example included, "I will exert considerable effort in learning this
material" ( 1 = strongly disagree to 7 = strongly agree; as =
.75 initially and .83 postfeedback).
Expectancy. We used both expectancy-based and self-efficacy-based measures to tap this construct. The expectancy measure consisted of two items from Lawler and Suttle (1973). We
asked participants to indicate how true it was that the first phrase
of a pair leads to the second phrase of a pair, for them personally,
in the context of the course. Pairs included, (a) "trying hard --*
Please note that this goal will in no way prevent you from
attaining a higher grade in this course. It is not an indication
of the grade you are capable of receiving, only a guideline
that you can use to track your performance. Your instructor
has no knowledge of your goal, and achievement of the
goal (or lack of achievement) will have no bearing on your
grade whatsoever. Good luck this semester!
3 Eight students had GPAs of 3.76 of higher, which created a
ceiling for their grade goals because students could not earn
more than 100% in the course. Thus, all analyses were run with
and without this group. In no cases did the results differ in
either significance level or effect size.
Goal setting theory suggests that people perform well when
they are assigned difficult and specific goals. Research has
found that a difficult goal for a student's performance in a
course is approximately .25 points higher than his or her
cumulative grade point average. Based on your grade point
average, we have given you a difficult and specific goal for
this course.
660
RESEARCH REPORTS
learning the material" and (b) "putting forth effort --* gaining
an understanding of the material." We assessed self-efficacy
with three items from Mclntire and Levine (1984). One example included, " I do well in activities where I have to remember
lots of information ''4 (1 = never to 7 = always; a s = .66
initially and .70 postfeedback).
Valence. We used three items adapted from Lawler and Suttle (1973) to assess valence. We asked participants to indicate
"how desirable" (in the context of the course) they considered
(a) "getting a good grade," (b) "doing well in the course,"
and (c) "achieving success in the class" to be ( 1 = very undesirable to 7 = very desirable; as = .63 initially and .64
postfeedback).
Conscientiousness. We used the 12-item scale from the
NEO Five-Factor Inventory, a shorter version of the NEO Personality Inventory (Costa & McCrae, 1992), to measure conscientiousness (1 = strongly disagree to 7 = strongly agree; a =
.79 ). Costa and McCrae (1992) reported test-retest reliabilities
of .83 for this scale, suggesting that the variable has a definite
trait quality.
Goal orientation. We used Button et al.'s (1996) eight-item
dispositional goal orientation measures. Learning orientation
items included, "I prefer to work on tasks that force me to learn
new things" and "The opportunity to do challenging work is
important to me." Performance orientation items included,
"Once I find the right way to do something, I stick to it" and
"The things I enjoy the most are the things I do best." (1 =
strongly disagree to 7 = strongly agree; as = .83 for leaming
orientation and .80 for performance orientation). Button et al.
provided reliability and construct validity evidence for these
scales. We computed coefficient alphas by treating each of the
three learning orientation and performance orientation scores
(i.e., initial, midpoint, and conclusion) as one item in a threeitem scale.
Prefeedback learning. We assessed prefeedback learning
through the first course examination. This examination consisted
of 60 items (100 points) that assessed declarative knowledge.
Postfeedback learning. We assessed postfeedback learning
through the final course examination, which again consisted of
60 items (100 points) that assessed declarative knowledge.
Control variables. Three control variables were used. GPA
served as a control to ensure that performance or motivation
variance due to ability could be captured. This control was
especially critical because the .25-point increase in grade
seemed more difficult to learners with low GPAs. We also coded
class membership because it was likely a contaminant that explained performance and motivation variance due to nonrandom
assignment of students to classes. A score of 0 was given to
students in Jason A. Colquitt's class; a score of 1 was given to
students in Marcia J. Simmering's class. Finally, we controlled
for goal commitment to statistically ensure that all learners were
equally committed to their assigned goals, given that such commitment should relate to performance and motivation, particularly after feedback is delivered. Indeed, Bobko and Colella
(1994) noted that reactions to performance standards should
depend on the degree to which those standards are accepted by
the individual. Thus, we used Hollenbeck, Klein, O'Leary, and
Wright's (1989) four-item goal commitment scale ( 1 = strongly
disagree to 7 = strongly agree; a s = .69 initially and .69
postfeedback), and it should be noted that commitment levels
were generally high (M = 5.11 out of 7; SD = 1.02).
Results
D e s c r i p t i v e Statistics
The means, standard deviations, and zero-order intercorrelations o f all variables are reported in Table 1. Coefficient alphas are shown in the diagonal o f the correlation
matrix. Several correlations o f variables never included
together in a single study are notable. For example, conscientiousness was positively associated with learning orientation levels and negatively associated with performance
orientation levels. Goal c o m m i t m e n t was positively related to motivation to learn, as well as valence and expectancy. Finally, conscientiousness had a significant positive
correlation with goal c o m m i t m e n t and learning, whereas
performance orientation was negatively correlated with
learning ( p o s t f e e d b a c k ) .
Tests o f H y p o t h e s e s
Hypotheses 1 - 3 predicted that the relationships between the personality variables and motivation to learn
w o u l d be mediated by valence and expectancy. The results
are shown in Table 2. A l l three personality variables explained significant incremental variance in motivation to
learn b e y o n d the effects o f the control variables. Consistent with our predictions, the personality variables explained less variance in motivation to learn when the Expectancy X Valence variables were statistically controlled.
However, conscientiousness and learning orientation still
explained significant incremental variance, suggesting
that their relationships with motivation to learn were only
partially mediated. 5 The directions o f these relationships
were all as predicted (see Table 1): Conscientiousness
and learning orientation were positively correlated with
expectancy, valence, and motivation to learn, and performance orientation was negatively correlated with expectancy and motivation to learn. Contrary to predictions,
performance orientation was unrelated to valence.
To c o m p l e m e n t the above analyses, we e x a m i n e d the
4 We conducted a principal components factor analysis with
varimax rotation to test the unidimensionality of the larger expectancy variable. A one-factor solution fit the data, suggesting
that the expectancy and self-efficacy items could in fact be
combined.
5 The fact that the relationships between conscientiousness
and goal orientation and motivation to learn were only partially
mediated by Expectancy x Valence variables suggests that significant correlations with motivation to learn are not simply a
function of common method variance. Presumably, controlling
for other self-report variables would remove variance due to
common methods and same-source bias.
RESEARCH REPORTS
relationship between the three personality variables (entered simultaneously) and motivation to learn. Personality
variables explained an incremental 28% of the variance
in prefeedback motivation to learn (p < .001 ), with both
conscientiousness (/~ = .33, p < .001 ) and learning orientation (/~ = .38, p < .001 ) having significant independent
relationships. Personality variables explained an incremental 27% of the variance in postfeedback motivation
to learn (p < .001 ), again with both conscientiousness
(fl = .28, p < .01 ) and learning orientation (/~ = .40, p <
.001 ) having significant independent relationships. These
results have practical significance in addition to statistical
significance. To express this, we classified learners into
positive and negative trait patterns, where a positive pattern represented high learning orientation, high conscientiousness, and low performance orientation, and expressed
the correlation between this pattern and motivation to
learn using a binomial effects size display (Rosenthal &
Rosnow, 1991). Compared with learners with a negative
pattern, learners with a positive pattern were 38% more
likely to be motivated before feedback and 40% more
likely to be motivated after feedback. They were also 30%
more likely to perform well on the first exam and 28%
more likely to perform well on the second. 6
Hypotheses 4 - 6 predicted interactions among the personality variables and prefeedback learning levels for
postfeedback expectancy and valence. We assessed these
hypotheses using moderated multiple regression; results
are presented in Table 3. No interactions were demonstrated for conscientiousness. However, interactions were
demonstrated for learning orientation with postfeedback
expectancy and performance orientation with postfeedback valence, with the pattern matching that shown in
Figure 2.
Hypothesis 7 predicted that motivation to learn would
be positively related to pre- and postfeedback learning.
Initial motivation to learn explained an additional 2% of
the variance in prefeedback learning (/~ = .14, p < .05)
beyond the three control variables, which accounted for
45% of the learning variance. Postfeedback motivation to
learn added 4% of the variance explained for postfeedback
"a
t,~
II
=<
e!
o
I
rl
00
o
.-....=.~
r---
I
H
II
o
I
.,=
II
~ 1 1 1 1 1 1 1 1
.....
ol
g
~ . . ~ . . .
~1
661
o
I
i
g
ca,
r.~
...........
~
;>
-
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II
~ ~<~
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.
6 Rosenthal and Rosnow (1991) expressed practical significance by putting a criterion into dichotomous terms such as
"perform well" or "perform poorly" and "motivated" or "unmotivated" using median splits. This convention is applied here
because it is congruent with the context of many training programs in which some cutoff must be achieved to fulfill a requirement or move on to a subsequent course. Relationships with
this dichotomous criterion are then expressed in percentage
terms, where the percentages are calculated by 50% _+ r/2.
Thus, the correlation between our positive trait pattern and prefeedback learning of r = .30 translates into 65% (50 + 15) and
35% (50 - 15), or a 30% difference (65 - 35) in the likelihood
of performing well based on trait patterns.
662
RESEARCH REPORTS
Table 2
Variance Explained in Motivation to Learn by Personality Variables
Personality variable
Initial motivation to learn (DV)
Postfeedback motivation to learn (DV)
Predictor
Conscientious
Learning
oriented
Performance
oriented
Conscientious
Learning
oriented
Performance
oriented
1. Class a
2. GPA a
3. Goal commitment"
4. Personality variable
Controlling for E × V
1. Class"
2. GPAa
3. Goal commitment~
4. Expectancy
5. Valence
6. Personality variable
.00
.00
.15"**
.28***
.00
.00
.15"**
.35***
.00
.00
.15"**
.17*
.02
.00
.11 **
.10"*
.02
.00
.11"*
.20***
.02
.00
.11 **
.04*
.00
.00
.15" **
.05**
.10"**
.08**
.00
.00
.15"**
.05**
.10"**
.13***
.00
.00
.15 ***
.05**
.10"**
.02
.02
.00
.11 **
.02
.22***
.05**
.02
.00
.11 **
.02
.22***
.09***
.02
.00
.11 **
.02
.22***
.02
Note. Values represent AR2s. N = 103. All equations had an overall F-statistic significance o f p < .001. GPA = cumulative grade point average;
E × V = Expectancy × Valence; DV = dependent variable.
"Control variables.
* p < .05. * * p < .01. * * * p < .001.
learning (/3 = .13, p < .05) beyond the three control
variables, which accounted for 54% of the learning variance. Thus Hypothesis 7 was supported. The fact that the
hypothesis was supported despite the marginal significance of the motivation-to-learn correlations in Table 1
illustrates the importance of the control variables.
Table 3
Regression Results for Moderation Hypotheses
Personality variable
Predictor
Conscientious
Learning
oriented
Performance
oriented
DV = Postfeedback expectancy a
1.
2.
3.
4.
5.
6.
7.
Class h
GPA b
Goal commitmentb
Initial expectancy
Personality variable
Learning
Personality × Learning
,00
.04*
.04"
.30"**
,01
.04*
.01
.00
.04*
.04*
.30"**
.02
.03*
.04**
.00
.04*
.04"
.30***
.00
.03*
.00
DV = Postfeedback valence ~
1.
2.
3.
4.
5.
6.
7.
Class ~
GPA b
Goal commitmentb
Initial valence
Personality variable
Learning
Personality × Learning
.00
.00
.03
.53***
.01
.00
.00
.00
.00
.03
.53***
.00
.00
.00
.00
.00
.03
.53***
.00
.00
.02*
Note. Values represent ARes. N = 103. All equations had an overall
F-statistic significance of p < .001. GPA = cumulative grade point
average; DV = dependent variable.
"Hypotheses 4, 5, and 6--expectancy. b Control variables, c Hypotheses 4, 5, and 6--valence.
* p < .05. * * p < .01. * * * p < .001.
Discussion
The positive relationships of learning orientation and
conscientiousness with motivation to learn stand out as
the most important contributions of this study. Learners
who had high levels of these personality variables exhibited higher motivation levels, both initially and in the wake
of feedback during the learning process. Furthermore, we
elucidated the underlying mechanisms for these effects
using an Expectancy x Valence framework, placing these
relationships in a more established theoretical context.
Specifically, the relationships of conscientiousness and
motivation to learn were partially mediated by expectancy
and valence. In other words, individuals who were reliable, self-disciplined, and persevering were more likely
to perceive a link between effort and performance and
were more likely to value high performance levels. These
results therefore complement past research by identifying
mechanisms for positive manifestations of this personality
variable (Barrick & Mount, 1991; Barrick et al., 1993).
Learning orientation was also related to motivationto-learn levels, with highly learning-oriented individuals
exhibiting higher expectancy and valence levels. In contrast, performance orientation was negatively related to
motivation to learn by means of expectancy. Thus, this
study is among the first to demonstrate a significant link
between goal orientation and two of the most visible motivational constructs, expectancy and valence. Our work
therefore places goal orientation (and conscientiousness)
in a larger, metatheoretical framework of Expectancy ×
Valence theories, allowing their relationships to be compared and contrasted with those of other trait and motivation variables (Kanfer, 1991).
RESEARCH REPORTS
Besides predicting motivation levels, the personality
variables we examined also moderated individuals' responses to performance levels during the learning process.
Although the role of personality in reacting to performance levels and feedback has been much discussed, empirical work is lacking (Fedor, 1991). Our work begins
to fill this gap by showing that the tendency for low performance levels to be associated with lower expectancy levels was less prevalent for highly learning-oriented individuals. This finding is consistent with past goal orientation
research suggesting that learning orientations buffer learners against the negative effects of early difficulty (Button
et al., 1996; Dweck & Leggett, 1988; Elliott & Dweck,
1988). Furthermore, the tendency for low performance to
be associated with lower valence was not as prevalent for
less performance-oriented individuals, an effect that has
not been demonstrated in past research. Thus, in situations
in which difficulty in mastering training content is expected, our results suggest that highly learning-oriented
individuals (and less performance-oriented individuals)
should remain motivated. We observed no interactions
for conscientiousness; rather, its effects were uniformly
positive across performance levels.
Taken together, these results have many practical implications. As noted above, learners with a trait pattern that
was highly conscientious, highly learning oriented, and
less performance oriented were 30% more likely to perform well on the first exam and 28% more likely to perform well on the second using Rosenthal and Rosnow's
(1991) convention. Because conscientiousness is used in
many selection batteries and has shown considerable stability (test-retest r = .83; Costa & McCrae, 1992), information about the conscientiousness of learners could be
a valuable component of the person analysis phase of
needs assessment (Goldstein, 1991). Goal orientation
measures could be used in a similar fashion, or the training
context could be manipulated to enhance one orientation
or the other. Dweck (1975) and Kraiger, Ford, and Salas
(1993) noted that situational cues can affect a learner's
orientation at a given point in time, giving a state component to the construct (Button et al., 1996; DeShon &
Fisher, 1996). Thus, in situations in which training materials are particularly complex and learning difficulty is expected, conscientious and learning-oriented individuals
could be selected for training, or a learning-oriented emphasis could be created by illustrating task experimentation and exploration of multiple learning strategies. In
situations in which conscientious individuals are lacking
or a learning-oriented emphasis is impossible, training
may need to be lengthened and learning rate slowed so
students can avoid early difficulty. Also, given the lower
valence levels among such individuals, external incentives
may be necessary to improve their motivation to learn.
A potential limitation of this study is that the learning
occurred in a classroom setting rather than in an actual
663
organizational training environment. However, because
we were interested in the effects of personality variables
in an achievement-oriented setting, the classroom did afford a high degree of fidelity. Specifically, Farr et al.
(1993) noted that most of the goals advanced in organizations are normative performance goals. Such goals are
also most frequently used in training programs, which
often culminate in objectively scored achievement tests.
Nonetheless, future research is needed to replicate these
findings in other types of learning environments, including
those in which explicit rewards are tied to goal achievement. Although many goals in organizational settings are
only loosely tied to objective rewards, the presence of such
rewards could increase the motivational ramifications of
outcome valence. In the current study, learners received
no specific awards for goal achievement. However, to the
extent that awards increase goal commitment (e,g., Hollenbeck & Klein, 1987), controlling for goal commitment
in all our analyses suggests that our results may be generalizable to such contexts.
In addition, researchers should replicate these findings
with other types of goals (e.g., mastery goals) and with
other forms of feedback. Fedor ( 1991 ) notes that feedback
is multidimensional, including facets such as sign, accuracy, timing, quantity, and specificity. To this we would add
that feedback can be subjective or objective, process or
outcome oriented, task or person generated, interactionally
fair or unfair (Bies & Moag, 1986), written or oral, individual or group level, or surprising or expected. Fedor notes
that individual differences affect preferences for certain
dimensions of feedback. It may be that personality variables
moderate reactions to feedback differently according to
these dimensions. For example, learning orientation may
be a more significant moderator when process feedback is
given or when feedback is task generated. Conscientiousness may be more necessary when subjective, interactionally unfair feedback is given. Performance orientation
may be more deleterious to individuals who receive negative feedback when it is surprising to them. Moreover, our
results may become less significant when feedback is general, brief, delayed, or inaccurate because the information
needed to alter behavior is not provided. These types of
feedback occur across a variety of training settings, depending on whether procedural or declarative knowledge
is emphasized (Kraiger et al., 1993), whether tasks amenable to objective feedback are used, and whether trainers
are experienced in feedback delivery. Future research on
personality effects in these domains is warranted.
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Revision received January 20, 1998
Accepted January 20, 1998 •