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THE DYNAMICS OF STATE GOAL ORIENTATIONS:
EXAMINING THE ROLES OF AFFECT AND PERFORMANCE EVALUATIONS
By

Brad Anthony Chambers

A THESIS
Submitted to
Michigan State University
in partial fulﬁllment of the requirements
for the degree of
MASTER OF ARTS
Department of Psychology

2001

ABSTRACT

THE DYNAMICS OF STATE GOAL ORIENTATIONS:
EXAMININ G THE ROLES OF AFFECT AND PERFORMANCE EVALUATION 8

By

Brad Anthony Chambers

The current study examined the extent to which individuals’ state goal orientations
change over time, particularly in response to receiving positive or negative performance
feedback. A process model including individuals’ performance evaluations, affective
reactions, and self-efﬁcacy was proposed and tested. In addition, the current study
examined potential antecedents to individuals’ initial state goal orientations (i.e., when
they ﬁrst encounter a situation). These antecedents included trait goal orientations,
situational inﬂuences (a goal orientation manipulation), and pre-task self-efﬁcacy.
Subjects engaged in a 5-cue Multiple Cue Probability Learning task resembling the stock
market. Results provided mixed support for the proposed process model of state goal

orientations. Theory and implications are discussed.

TABLE OF CONTENTS

LIST OF TABLES .................................................................................. vii
LIST OF FIGURES ................................................................................. ix
INTRODUCTION .................................................................................... 1
Goal Orientation .................................................................................. 5
Dimensionality of goal orientations ...................................................... 10

Two factors or three? ..................................................................... 11
Parallel Theories to Goa] Orientations ....................................................... 12
Self-discrepancy theory ................................................................... 12
Control theory .............................................................................. 14

State vs. Trait .................................................................................... 15
Person by situation interaction ............................................................ 17

State and trait goal orientation ............................................................ l7
Manipulations of state goal orientations ................................................ 19
Strong needs and desires lead to robust states .......................................... 20

The malleability of personality ........................................................... 22
Inﬂuence of self—efﬁcacy on state goal orientations ................................... 25
Summary of state and trait goal orientations ........................................... 27
Self-Set Goals, Performance, and Feedback ................................................ 28
Goal setting leads to performance, and performance leads to feedback ............ 28
Internal and external sources of feedback ............................................... 28

How does an individual choose a goal? ................................................ 29
Performance Evaluations ...................................................................... 3O
Weiner’s contributions ..................................................................... 30
Success vs. failure .......................................................................... 31

State and trait goal orientations and performance evaluations ........... ’ ............ 32
Summary of performance evaluations ................................................... 33
Affective Reactions to Feedback ............................................................. 33
State and trait goal orientations and affective reactions .............................. 34
Performance evaluations and affect ...................................................... 35
Summary of affective reactions to feedback ............................................ 36

State Goal Orientations Over Time ........................................................... 36
Self-efﬁcacy revisited ..................................................................... 37

Affect revisited .............................................................................. 38
Research Hypotheses ........................................................................... 39
Factors leading to initial state goal orientations ....................................... 39
Effects of goal orientations and feedback on performance evaluations ............ 42
Effects of goal orientations and feedback on affect ................................... 43
Dynamic processes behind state goal orientations .................................... 45
METHOD .................................................. 48

iii

Study Design .................................................................................... 48

Subjects .......................................................................................... 48
Task Overview and Criterion Measure ...................................................... 49
Goal Orientation Manipulations .............................................................. 50
Positive vs. Negative Feedback Manipulation .............................................. 52
Measures ......................................................................................... 52
State goal orientations ..................................................................... 52
Trait goal orientations ..................................................................... 53
Self-efﬁcacy ................................................................................. 54
Openness to experience .................................................................... 54
Agreeableness .............................................................................. 54
Self-monitoring ............................................................................. 55
Performance evaluations .................................................................. 55
Affect ........................................................................................ 56
Feedback believability ..................................................................... 56
Demographics .............................................................................. 56
Procedure ........................................................................................ 57
RESULTS ............................................................................................ 58
Manipulation Checks .......................................................................... 58
Positive vs. negative feedback ............................................................ 58
Goal orientation manipulation ............................................................ 58
Independence of Goal Orientations .......................................................... 66
Relative Stability of Traits vs. States ........................................................ 66
Trait Goal Orientations, Situational Inﬂuences, and State Goal Orientations .......... 67
Mastery goal orientation .................................................................. 67
Prove goal orientation ..................................................................... 69
Avoid goal orientation ..................................................................... 69
Trait Goal Orientations, Individual Differences, and State Goal Orientations ......... 70
Mastery goal orientation .................................................................. 72
Prove goal orientation .......................... ' ........................................... 76
Avoid goal orientation ..................................................................... 77
Self-Efﬁcacy and Initial State Goal Orientations .......................................... 79
Self-Efﬁcacy and Subsequent State Goal Orientations .................................... 79
Mastery goal orientation .................................................................. 79
Prove goal orientation ..................................................................... 80
Avoid goal orientation ..................................................................... 81
State Goal Orientations, Feedback Sign, and Performance Evaluations ................ 82
Mastery goal orientation .................................................................. 82
Prove goal orientation ..................................................................... 87
Avoid goal orientation ..................................................................... 88
Trait Goal Orientations, Feedback Sign, and Performance Evaluations ................ 91
Mastery goal orientation .................................................................. 91
Prove goal orientation ..................................................................... 95
Avoid goal orientation ..................................................................... 98
State Goal Orientations, Feedback Sign, and Affective Reactions ..................... 103

Mastery goal orientation ................................................................. 103

Prove goal orientation .................................................................... 109

Avoid goal orientation ................................................................... 110

Trait Goal Orientations, Feedback Sign, and Affective Reactions ..................... 115
Mastery goal orientation ................................................................. 115

Prove goal orientation .................................................................... 119

Avoid goal orientation ................................................................... 122
Performance Evaluations and Affective Reactions ....................................... 123
Joy .......................................................................................... 124
Distress ..................................................................................... 124
Performance Evaluations and Subsequent Self-Efﬁcacy ................................. 125
Affective Reactions and Subsequent State Goal Orientations ........................... 126
Mastery goal orientation ................................................................. 126

Prove goal orientation .................................................................... 127

Avoid goal orientation ................................................................... 128
Summary of Results .......................................................................... 130
Predictors of initial state goal orientations ............................................. 130
Effects on performance evaluations and affective reactions ........................ 132
Self-efﬁcacy revisited .................................................................... 133
Affective reactions and subsequent state goal orientations ......................... 133
DISCUSSION ...................................................................................... 135
Summary of Research Goals ................................................................. 135
Review of Goal 1 Attainment ............................................................... 135
Review of Goal 2 Attainment ............................................................... 139
Future Directions .............................................................................. 142
Limitations .................................................... - ................................. 145
Implications and Conclusion ................................................................. 146
Appendix A: Sample Trial ........................................................................ 149
Appendix B: Pilot Study .......................................................................... 150
Appendix C: State Goal Orientations ........................................................... 152
Appendix D: Trait Goal Orientations ........................................................... 153
Appendix E: Self-Efﬁcacy ....................................................................... 154
Appendix F: Openness to Experiences ......................................................... 155
Appendix G: Agreeableness ..................................................................... 156
Appendix H: Self-Monitoring .................................................................... 157
Appendix 1: Performance Evaluations .......................................................... 159

Appendix J: Affect (Elation vs. Depression; Relief vs. Anxiety) .......................... 160

Appendix K: Feedback Believability ............................................................ 161
Appendix L: Demographics ...................................................................... 162
Appendix M: Informed Consent ................................................................. 163
Appendix N: Task Instructions .................................................................. 164
Appendix 0: Debrief .............................................................................. 168
REFERENCES .................................................................................... 169

vi

LIST OF TABLES

Table 1. Means, standard deviations, and intercorrelations among variables .............. 59

Table 2. Summary of hierarchical regression analyses for trait goal orientations,
situational inﬂuences, and state goal orientations .............................................. 68

Table 3. Summary of hierarchical regression analyses for trait mastery goal orientation,
individual differences, and state mastery goal orientation .................................... 73

Table 4. Summary of hierarchical regression analyses for trait prove goal orientation,
individual differences, and state prove goal orientation ....................................... 74

Table 5. Summary of hierarchical regression analyses for trait avoid goal orientation,
individual differences, and state avoid goal orientation ....................................... 75

Table 6. Summary of hierarchical regression analyses for state mastery goal orientation,
feedback sign, and performance evaluations .................................................... 83

Table 7. Summary of hierarchical regression analyses for state prove goal orientation,
feedback sign, and performance evaluations .................................................... 84

Table 8. Summary of hierarchical regression analyses for state avoid goal orientation,
feedback sign, and performance evaluations .................................................... 85

Table 9. Summary of hierarchical regression analyses for trait mastery goal orientation,
feedback sign, and performance evaluations .................................................... 92

Table 10. Summary of hierarchical regression analyses for trait prove goal orientation,
feedback sign, and performance evaluations .................................................... 93

Table 11. Summary of hierarchical regression analyses for trait avoid goal orientation,
feedback sign, and performance evaluations .................................................... 94

Table 12. Summary of hierarchical regression analyses for state mastery goal
orientation, feedback sign, and the experience of joy ......................................... 105

Table 13. Summary of hierarchical regression analyses for state prove goal orientation,
feedback sign, and the experience of joy ........................................................ 106

Table 14. Summary of hierarchical regression analyses for state avoid goal orientation,
feedback sign, and the experience of distress .................................................. 107

vii

Table 15. Summary of hierarchical regression analyses for trait mastery goal orientation,
feedback sign, and the experience of joy ........................................................ 116

Table 16. Summary of hierarchical regression analyses for trait prove goal orientation,
feedback sign, and the experience of joy ........................................................ 117

Table 17. Summary of hierarchical regression analyses for trait avoid goal orientation,
feedback sign, and the experience of distress .................................................. 118

viii

LIST OF FIGURES

Figure 1. Conceptual model ........................................................................ 6
Figure 2. Operational model ...................................................................... 40

Figure 3. Interaction between trait avoid goal orientation and goal orientation
manipulation condition on state avoid goal orientation (Time 1) ............................ 71

Figure 4. Interaction between state prove goal orientation and feedback sign on
performance evaluations (Time 3) ................................................................. 89

Figure 5. Interaction between trait prove goal orientation and feedback sign on
performance evaluations (Time 2) ................................................................. 97

Figure 6. Interaction between trait prove goal orientation and feedback sign on
performance evaluations (Time 3) ................................................................ 99

Figure 7. Interaction between trait avoid goal orientation and feedback sign on
performance evaluations (Time
1) ..................................................................................................... 101

Figure 8. Interaction between trait avoid goal orientation and feedback sign on
performance evaluations (Time 2) ............................................................... 102

Figure 9. Interaction between trait avoid goal orientation and feedback sign on
performance evaluations (Time 3) ............................................................... 104

Figure 10. Interaction between state prove goal orientation and feedback sign on joy
(Time 3). ........................................................................................... ll 1

Figure 1]. Interaction between state avoid goal orientation and feedback sign on distress
(Time 1) ............................................................................................ 112

Figure 12. Interaction between state avoid goal orientation and feedback sign on distress
(Time 3) ............................................................................................ 114

Figure 13. Interaction between trait prove goal orientation and feedback sign on joy
(Time 3) ............................................................................................ 121

Figure 14. Summary of supported linkages in the theoretical and operational
models ............................................................................................... 131

ix

INTRODUCTION

The goals that individuals set for themselves or strive to achieve can have
substantial effects on individuals’ current and future performance on a task. These
effects can express themselves in the extent to which individuals persist in the face of
failure and learn from their mistakes or become discouraged by failure and withdraw
from the task. Researchers have examined these patterns of effects in education, sport,
and work settings, and their results have become the foundations of goal orientation
theory.

Recent research has conceptualized goal orientations as three separate factors:
mastery, performance-prove (prove), and performance-avoid (avoid). Speciﬁcally,
mastery goal orientation refers to “the desire to develop the self by acquiring new skills,
mastering new situations, and improving one’s competence” (VandeWalle, 1997, p.
1000). Further, prove goal orientation refers to “the desire to prove one’s competence
and to gain favorable judgments about it” (V andeWalle, 1997, p. 1000). Finally, avoid
goal orientation refers to “the desire to avoid the disproving of one’s competence and to
avoid negative judgments about it” (VandeWalle, 1997, p. 1000).

Indeed, one could argue that goal orientation theory is based on a renewed interest
in achievement motivation, and it has several implications for the workplace. For
example, research has shown that individuals’ goal orientations inﬂuence the tasks that
they choose to engage in. Elliott and Dweck (1988) found that high levels of mastery
goal orientation were associated with individuals seeking out challenging tasks from

which they could learn new skills. High levels of performance goal orientations (prove

and avoid), on the other hand, were associated with individuals seeking out less
challenging tasks (Elliott & Dweck, 1988).

Further, individuals’ goal orientations have implications for their reactions to
negative feedback. For example, individuals with high levels of mastery goal orientation
and low levels of performance goal orientations (prove and avoid) tend to interpret
negative feedback in a task-oriented manner, using the feedback to help improve their
performance in the future (Kluger & DeNisi, 1996). In contrast, individuals with high
levels of performance goal orientations (prove and avoid) and low levels of mastery goal
orientation tend to interpret feedback as a self—directed criticism (Kluger & DeNisi,
1996).

Finally, goal orientations appear to have implications for individuals’ cognitive
engagement in tasks. Speciﬁcally, Greene and Miller (1996) and Meece, Blumenﬁeld,
and Hoyle (1988) note that high levels of mastery goal orientation are associated with
deeper, more meaningful processing of task information. In contrast, these researchers
note that high levels of performance goal orientation (prove and avoid) are associated
with shallow, more superﬁcial processing of task information.

Thus, the goal orientations that individuals possess have implications for a variety
of work- and achievement-related situations. In general, the discussion thus far has
suggested that a high level of mastery goal orientation is superior in all situations.
However, this is not necessarily the case. For example, individuals working in
environments where mistakes are disastrous (e.g., a nuclear submarine) must always
strive to perform well and avoid mistakes at all costs. Therefore, one must consider the

situation when evaluating which goal orientations are most ‘important.’

Factors leading to the extent to which individuals are motivated by each of the
aforementioned goal orientations have been discussed in terms of trait goal orientations
and state goal orientations. The former refers to an individual’s tendency to adopt
mastery-, prove-, or avoid-oriented goals on average. In contrast, state goal orientations
refer to the extent to which the goals that an individual adopts in a speciﬁc situation are
mastery-, prove-, or avoid-oriented in nature. Further, theorists recognize the inﬂuence
of trait goal orientations on state goal orientations, but they also recognize the importance
of situational inﬂuences such as task instructions and the task environment.

Researchers who have examined state goal orientations, however, have rarely
considered the potential inﬂuences of other individual differences besides trait goal
orientations. For instance, individual’s who have higher levels of openness to experience
may be more adaptable to the situation compared to individuals with lower levels of this
construct—Le, they may be more likely to adopt levels of state goal orientations that are
inconsistent with their trait goal orientations. Fisher (1998) examined the interaction
between an individual’s trait mastery goal orientation and his/her interest on a task in the
prediction of his/her level of state mastery goal orientation, but even this attempt did not
consider the potential effects of other individual differences. (Further, the
conceptualization of goal orientation in Fisher’s work did not distinguish performance-
prove from performance-avoid.)

The ability to predict and successfully manipulate individual’s state goal
orientations is advantageous in both training and performance settings. For example, the
coach of a baseball team may wish to elicit a strong mastery goal orientation while

individuals undergo training—he/she is likely to want the players to learn the game well

and master all of the necessary skills. However, the coach may then wish to elicit a
strong prove goal orientation during a game—the players need to do well and win the
game. As one would expect, the same principles could apply to many other situations
and jobs as well. Thus, being able to identify the factors leading to individuals’ state goal
orientations is desirable in various settings, and current research provides only a partial
explanation of this phenomenon.

After an individual’s initial state goal orientations are determined, an important
question arises: What happens to state goal orientations over time? Once an individual’s
state goal orientations are determined for a given situation, will he/she maintain those
state goal orientations over time, or will his/her trait goal orientations inﬂuence
subsequent state goal orientations (speciﬁcally, after receiving negative feedback)? If the
effects of trait goal orientations continue to inﬂuence state goal orientations over time,
this suggests that managers can not simply train their employees to adopt particular state
goal orientations, but that they should take into consideration individuals’ trait goal
orientations during the selection process.

Again, past research offers little explanation in terms of the dynamic processes
related to state goal orientations. Fisher (1998) provides a process model of goal
orientations over time, but this model is not sufﬁcient. While her model does provide
insight into the dynamic aspect of state goal orientations as a function of performance
feedback, it does not explain how feedback inﬂuences state goal orientations. In order to
understand fully the dynamics of state goal orientations, we must understand the

processes involved.

Figure 1 presents the conceptual model upon which this thesis is based. The
model, proposes that initial state goal orientations are inﬂuenced by self-efﬁcacy as well
as interactions between trait goal orientations and situational factors and between trait
goal orientations and a variety of individual differences. These state goal orientations
then lead to individuals’ goal setting and subsequent performance. Based on
performance, individuals receive either positive or negative feedback, which interacts
with both state goal orientations and trait goal orientations in the prediction of
individuals’ performance evaluations and affect. In addition, there is a proposed
temporal relationship between performance evaluations and affect; speciﬁcally,
evaluations precede affect. These performance evaluations and affect then inﬂuence
subsequent state goal orientations (the link between performance evaluations and
subsequent state goal orientations is mediated by self-efﬁcacy), and the cycle repeats
itself.

In the sections that follow, each of the components in the conceptual model (see
Figure 1) is discussed, relevant literature is summarized, theoretical and rational
arguments are presented, and an operational model and list of testable hypotheses are
proposed. Before the speciﬁc elements in the model can be addressed, however, a more
in-depth review of the history behind the goal orientation construct is in order.

Goal Orientation

Early work by Dweck and Reppucci (1973) documented the existence of two
patterns of reactions and subsequent performance that competent individuals typically
exhibit after repeated failure. They observed that individuals who displayed the largest

performance decrements were those who assumed the least responsibility for their

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performance and, when they did accept responsibility for their performance, tended to
attribute both success and failure to ability rather than effort. That is, these individuals
consistently attributed success to high ability and failure to lack of ability—something
that is not under their immediate control. Thus, after repeated failure, these individuals
gave up because they believed that there was nothing that they could do to succeed. In
essence, these individuals exhibited symptoms of learned helplessness—the passive,
withdrawn behavior that an individual displays after being exposed to seemingly
unavoidable aversive events (Seligman, 1975). In contrast, individuals who persisted in
the face of failure and who displayed the least performance decrements were those who
consistently attributed their performance to effort expenditure. That is, they had a
consistent, trait-like belief that success was the result of much effort and that failure was
the result of too little effort, and they behaved accordingly.

In a follow—up study, Dweck (1975) examined the effects of an attribution
retraining program on individuals’ subsequent performance following repeated failure.
The retraining program taught “helpless” children (i.e., individuals who consistently
show symptoms of withdrawal following failure) to take responsibility for their
performance and to attribute their performance to effort expenditure rather than
underlying ability. These individuals were compared to a second group of “helpless”
children who were not subjected to the attribution retraining program. Instead, they were
presented with a series of trials during which they received success feedback (based on
the notion that success feedback would increase their perceived competence, thus
increasing their performance). Whereas individuals in the latter group demonstrated

continued performance decrements following the success-only trials, individuals who

received the attribution retraining maintained or improved their performance. Thus,
Dweck (1975) provides support for Dweck and Reppucci’s (1973) conclusion that
individuals can make different attributions for their performance and that these
differences lead to potentially different performance outcomes.

In addition, Dweck (1975) provides support for the distinction between state and
trait goal orientations. Individuals who did not receive the attribution retraining exhibited
motivation and performance consistent with their dispositional, helpless tendencies. In
contrast, individuals who received the attribution retraining exhibited motivation and
performance consistent with the retraining. Thus, the motivation and performance that
these individuals actually exhibited (i.e., state) was or was not consistent with their
dispositional tendencies (i.e., trait), depending on the presence/absence or situational
inﬂuences. (This study also provides evidence of situational inﬂuences on goal
orientations and the ability to manipulate goal orientations, both of which are discussed in
later sections.)

Later work by Diener and Dweck (1978, 1980) provides support for earlier
ﬁndings (i.e., Dweck, 1975; Dweck & Reppucci, 1973) that some individuals adopt a
helpless response pattern following failure whereas other individuals adopt more of an
improvement-focused attitude. In fact, Diener and Dweck (1978, 1980) referred to the
former group as “helpless” (maintaining the label from previous work by Dweck (1975)
and Dweck and Reppucci (1973) and the latter group as “mastery-oriented.” Also of
importance, Diener and Dweck (1980) observed differences between these individuals in
the extent to which they viewed success as predictive of future success. Speciﬁcally,

helpless individuals do not believe that current success will lead to future success

whereas the opposite is true for mastery-oriented individuals. Essentially, helpless
individuals view their success as “less successful” compared to mastery-oriented
individuals (Diener & Dweck, 1980).

Independent from Dweck and her colleagues, Nicholls developed a theory of
motivation that closely resembles the ﬁndings and conclusions of Dweck’s research.
Nicholls (1979, 1983, 1984) distinguished self—evaluation reﬂecting ego-involvement
from self-evaluation reﬂecting task-involvement. Ego-involvement, according to
Nicholls (1984), refers to an individual’s desire to demonstrate his/her ability to others
and avoid appearing incompetent, whereas task-involvement refers to the extent to which
an individual regards personal mastery as more important than the demonstration of
ability. Individuals in the former group tend not to persist in the face of failure, for others
could interpret it as a sign of incompetence; instead, they tend to withdraw from the task.
Individuals in the latter group, in contrast, persist in the face of difﬁculty and maintain
personal mastery as their primary goal.

In a 1986 review of the motivation literature, Dweck (1986) notes the
convergence of ideas between her work and the work of Nicholls as discussed above.
She uses the term goal orientation to describe the general patterns of goals that
individuals adopt. Speciﬁcally, Dweck (1986) describes a learning (mastery) goal
orientation as one that leads an individual to adopt learning (mastery) goals—the
individual seeks to increase his/her competence by understanding and mastering
something new. In contrast, a performance goal orientation is described as one that leads
an individual to adopt performance goals—the individual seeks to gain positive

judgments and avoid negative judgments regarding his/her competence (Dweck, 1986).

Similar to Dweck (1986), Ames and Archer (1988) and Harackiewicz and Elliot
(1993) note the striking similarities between the ﬁndings and conclusions of the different
researchers discussed thus far. In addition to the researchers already mentioned, Dweck
and Leggett (1988) discuss learning and performance goals; Koestner, Zuckerman, and
Koestner (1987), Ryan (1982), and Sansone (1986) distinguish task-involvement from
ego-involvement; and Ames (1984) and Butler ( 1992) contrast mastery and ability goals.
As Ames and Archer (1988) note, these researchers have simply attached their own labels
to well-known constructs: mastery and performance goal orientations.

Dimensionality of goal orientations. To this point, no explicit mention has been
made regarding the dimensionality of goal orientation. Initial research (e.g., Dweck,
1986; N icholls, 1984) conceptualized goal orientation as a unidimensional construct with
mastery goal orientation and performance goal orientation as the endpoints. Under this
conceptualization, then, an individual is thought of as being either mastery-oriented or
performance-oriented. If this were true, one would expect the correlation between trait
measures of mastery and performance goal orientations to be highly negative— -1.0,
ideally. Research does not support this supposition, however. Instead, moderately low
correlations between these two goal orientations are often observed. For instance, Duda
and Nicholls (1992) observed correlations ranging from -.32 to .21 between mastery and
performance goal orientations in a sample of high school students; and Hofmann and
Strickland (1995) and Phillips and Gully (1997) observed correlations of .27 and -.10
(respectively) between these two goal orientations among samples of college students.

These low correlations between individuals’ mastery and performance goal

orientations suggest that the two goal orientations actually lie on two separate continuums

10

rather than a single continuum. Rather than an individual being just mastery-oriented or
just performance-orientated, then, individuals can actually possess varying degrees or
each goal orientation. Thus, while it is still possible for an individual to be just mastery-
oriented (i.e., very high mastery goal orientation and very low performance goal
orientation) or just performance-oriented (i.e., very high performance goal orientation
and very low mastery goal orientation), he/she may have less extreme levels of both goal
orientations. For instance, an individual may have a moderate level of mastery goal
orientation and a slightly higher level of performance goal orientation—or he/she may be
very high on both goal orientations.

Based on the argument presented above, it is generally inaccurate to refer to
individuals as “mastery—oriented” or “performance-oriented” as so many researchers have
done in the past. Hofmann and Strickland (1995) note that the reason why researchers
have become accustomed to referring to individuals in such a way is due in large part to
the fact that most studies examining goal orientations have been experimental studies
where mastery and performance goal orientations are manipulated one at a time. (The
ability to manipulate individuals’ goal orientations is discussed in a later section.)

Two factors or three? Thus far, it has been established that the goal orientation
construct consists of two factors (i.e., mastery and performance) which are orthogonal to
each other. Recent researchers (e. g., Elliot, 1999; Elliot & Harackiewicz, 1996;
Middleton & Midgley, 1997; Midgley et al., 1998; VandeWalle, 1997), however, have
argued that the current conceptualization of goal orientation is inadequate. They argue
that a third kind of goal orientation must be considered in order to understand and predict

more accurately individuals’ motivation and performance. Speciﬁcally, they contend that

11

performance goal orientation should be broken down into performance-prove and
performance-avoid.

As noted above, Nicholls’ (1984) initial conceptualization of ego involvement—
the conceptual equivalent of performance goal orientation—included the individual’s
desire to demonstrate his/her ability to others as well as avoid appearing incompetent.
Similarly, Dweck’s (1986) initial conceptualization of performance goal orientation
included his/her desire to seek judgments of competence from others as well as avoid
judgments of incompetence from others. Indeed, this distinction between approach (i.e.,
prove) motivation and avoid motivation dates back well before the days of Nicholls and
Dweck. In fact, Elliot (1999) traces this distinction back to the ancient Greek
philosophers Democritus (460-370 B.C.), Aristippus (430-360 B.C.), and Epicurus (342-
270 B.C.), who talked of man’s hedonistic pursuit of pleasure and avoidance of pain.
Parallel Theories to Goal Orientations

Self-discrepancy theog. Higgins and his associates (Higgins, 1987; Higgins,
Bond, Klein, & Strauman, 1986) proposed a self-regulatory model of goal-directed
behavior that parallels the theory and ﬁndings of goal orientations research. Speciﬁcally,
Higgins argues that individuals’ goal-directed behaviors are motivated by one of two
discrepancies. The ﬁrst discrepancy is between an individual’s perceived actual self and
his/her ideal self, and the second discrepancy is between an individual’s perceived actual
self and his/her ought self.

According to Higgins, an ideal self is a desired self to which an individual aspires.
For example, an individual may aspire to be known as a caring and generous person.

Achieving this ideal self means that a desired reward (e.g., personal satisfaction) has been

12

attained. An ought self is a duty- or obligation-based self—an individual may feel that it
is his/her social obligation to donate 15% of his/her income to charity each year.
Fulﬁlling the desires of the ought self means that an undesired punishment (e.g., societal
disapproval) has been avoided. Finally, actual self refers to an individual’s analysis of
his/her current behavior and attitudes. For example, an individual may realize that he/she
currently donates less than one percent of his/her annual income to charity.

Thus, according to l-Iiggins’ theory and the example above, an individual who is
motivated by ideal-actual discrepancies will recognize that he/she currently gives less
than one percent of his/her annual income to charity and that this is inconsistent with
his/her desire to be a caring and generous person. This individual will be motivated to
give more money to charity. In contrast, an individual who is motivated by ought-actual
discrepancies will recognize that he/she currently donates less than one percent of his/her
annual income to charity and that this is ‘unacceptable’ according to social standard.
This individual will also be motivated to give more money to charity. Thus, although the
outcome for these two individuals is the same (i.e., both are motivated to donate more
money to charity), the motivational processes by which this outcome occurs differ
drastically.

Recall the deﬁnitions for mastery, prove, and avoid goal orientations. An
individual with a high level of avoid goal orientation is motivated to avoid the
demonstration of some negatively valenced outcome (i.e., the demonstration of lack of
ability). Based on the discussion above, such an individual is similar to someone who is
motivated by ought-actual discrepancies. Individuals with high levels of mastery and

prove goal orientations are motivated to achieve some positively valenced outcome (i.e.,

13

mastery of the current task or the demonstration of ability, respectively). Thus, such
individuals are similar to someone who is motivated by ideal-actual comparisons.

Control theog. Inﬂuenced by Higgins (1987; Higgins et al., 1986), Carver and
his associates (Carver, Lawrence, & Scheier, 1996; Carver & Scheier, 1995) recently
proposed a self-regulatory model whereby the goal-directed behaviors in which
individuals engage fall into one of two categories: discrepancy enlarging loop and
discrepancy reducing loop. A discrepancy enlarging loop—or positive feedback loop—
has as its focus some undesired end-state away from which an individual wants to move
(Carver & Scheier, 1995). That is, the individual is motivated to move away from some
negatively valenced reference point. Moreover, a discrepancy reducing loop—or
negative feedback loop—has as its focus some desired end-state toward which an
individual wants to move (Carver & Scheier, 1995). That is, the individual is motivated
to work toward some positively valenced reference point.

The motivation and thought processes that differentiate individuals engaging in
each of these two behavioral groups parallels fairly well with the discussion of goal
orientation theory as well as Higgins’ (1987; Higgins et al., 1986) self-discrepancy
theory. Recall from above, avoid goal orientation is associated with the desire to avoid
the demonstration of lack of ability. Thus, the reference point for this orientation is the
demonstration of lack of ability, and it has a negative valence associated with it. Hence,
individuals who engage in discrepancy enlarging loops are similar to individuals with
high levels of avoid goal orientations. These individuals are also similar to those who are

motivated by actual-ought self-discrepancies.

l4

As noted above, mastery goal orientation is associated with the desire to learn and
master the task at hand. Thus, the reference point for this orientation is the learning and
mastery of some task, and it has a positive valence associated with it. Similarly, prove
goal orientation is associated with the desire to demonstrate one’s competence to others.
The reference point for this orientation is the demonstration of one’s competence to
others, and it too has a positive valence associated with it. Hence, individuals who
engage in discrepancy reducing loops are similar to individuals with high levels of prove
and mastery goal orientations. These individuals are also similar to those who are
motivated by actual-ideal self-discrepancies.

State vs. Trait

As discussed by Mischel and Shoda (1995), a long-standing tradition in the ﬁeld
of psychology has been to conceptualize individual differences as traits—behavioral
dispositions that lead individuals to behave similarly across situations. Based on this
conceptualization, one would expect an individual with a high level of a particular
personality trait to behave in a manner consistent with that trait across situations.
Further, any deviation from this average behavior (i.e., trait) is viewed as unwanted or
uninforrnative variance—error variance (Mischel & Shoda, 1995). However, as Mischel
and Shoda (1995) argue, it is very informative when an individual behaves diﬁ’erently
than he/she would normally behave—it provides evidence of a person by situation
interaction.

Mischel and Shoda (1995) do not necessarily discount the relevance of traits;
instead, they view personality as a rational system consisting of tf-then scenarios. For

example, if a particular individual who normally behaves in an outgoing manner (i.e.,

15

extraversion) attends a party where he knows everyone, then he will behave in a very
outgoing manner. However, If this individual attends a party where he knows no one,
then he will remain relatively quiet. Such behavioral inconsistencies have led some (e.g.,
Block, 1995) to argue that personality theorists need to focus on understanding intra-
individual behavior before inter-individual behavior can be understood.

Arguments presented by Mischel and Shoda (1995) and Block (1995) support the
conceptualization of personality in terms of traits and states. Speciﬁcally, a state can be
deﬁned as an individual’s behavior or level of a personality characteristic in a speciﬁc
situation at a particular moment in time, whereas a trait can be deﬁned as an individual’s
average behavior or level of a personality characteristic. Thus, the current study adopts
of the conceptualization of traits as aggregates of states.

In order for traits to be aggregates of states, there must be some pattern or
consistency of behavior, but what leads to this consistency? That is, individuals behave
in relatively consistent manners across time and situations (otherwise, we would have no
reason for even entertaining the idea of, for example, the Big Five personality variables),
but something must lead to this behavioral consistency.

Many theorists have argued that individuals have underlying needs and desires,
and these needs and desires inﬂuence their behavior. For example, Murray (1951) argues
that individuals have manifest needs that lead them to behave in certain ways that satisfy
those needs. Higgins (1987; Higgins et al., 1986) argues that individuals have ideal
selves toward which they aspire. As such, individuals are motivated to behave in ways
that are consistent with their idealized self. Festinger (1957) argues for a need for

individuals to maintain consistency between their thoughts and behaviors. The list

16

continues, but all of these examples illustrate the notion that individuals have underlying
needs and desires, and these needs and desires lead individuals to behave in fairly
consistent ways across situations and time.

Person by situation interaction. Despite the simplicity of the previous paragraph,
however, individuals do not always behave consistently. The premise of a person by
situation interaction is that an individual’s behavior at a speciﬁc moment in time is a
product of his/her trait-like predispositions (i.e., needs, desires, etc.) and situational
inﬂuences. Speciﬁcally, individuals behave similarly in situations that they perceive to
be similar; in contrast, situations that are perceived to be different may elicit different
personality characteristics (Flavell, 1985; Mischel & Shoda, 1995; Pervin, 1989), leading
individuals to behave differently. There are differing perspectives, however, regarding
the role of the situation. One perspective is that individuals actively seek out certain
situations rather than others (Mischel & Shoda, 1995; Pervin, 1989). An alternative
perspective views the individual’s behavior as more reactionary compared to the
aforementioned, proactive perspective. According to this perspective, individuals
passively react to and respond to the situations that they encounter rather than actively
seek out certain situations (Mischel & Shoda, 1995; Pervin, 1989). Although both of
these perspectives have merit, the current study adopted the latter. By adopting the latter
perspective, this study cannot address questions related to the situations in which
different individuals choose to participate. Such questions must be addressed in a study
that allows individuals to choose their situations.

State and trait goal orientation. Similar to extraversion and other individual

difference variables, goal orientations can also be analyzed in terms of state and trait

l7

behaviors. Researchers can examine individuals’ state goal orientations (i.e., levels of
goal orientations that individuals assume in speciﬁc situations) as well as their trait goal
orientations (i.e., levels of goal orientations that individuals assume an average, across
time and situations). For the current study, the following deﬁnitions of state and trait
goal orientations were adopted:

State mastery goal orientation: In a given situation at a particular moment
in time, the extent to which one focuses his/her attention on
developing him/herself by acquiring new skills, mastering new
situations, and improving his/her competence.

Trait mastery goal orientation: The tendency (across time and situations)
to focus one’s attention on developing him/herself by acquiring
new skills, mastering new situations, and improving his/her
competence.

State prove goal orientation: In a given situation at a particular moment in
time, the extent to which one focuses his/her attention on proving
his/her competence to others and gaining favorable judgments
from them.

Trait prove goal orientation: The tendency (across time and situations) to
focus one’s attention on proving one’s competence to others and
gaining favorable judgments from them.

State avoid goal orientation: In a given situation at a particular moment in

time, the extent to which one focuses his/her attention on avoiding

18

the demonstration of incompetence and negative judgments from
others.

Trait avoid goal orientation: The tendency (across time and situations) to
focus one’s attention on avoiding the demonstration of
incompetence and negative judgments from others.

Manipulations of state goal orientations. As it has been alluded to above,
characteristics of the situation can inﬂuence individuals’ state goal orientations. Because
of this, researchers (e.g., Ames, 1984; Chambers, Steele-Johnson, & Mangos, 2000;
Elliott & Dweck, 1988; Elliot & Harackiewicz, 1996; Harackiewicz & Elliot, 1993;
Koestner & Zuckerman, 1994; Martocchio, 1994; Steele-Johnson, Beauregard, Hoover,
& Schmidt, 2000) often use task instructions to manipulate individuals’ state goal
orientations. (Of the cited examples, only Elliot and Harachiewicz [1996] conceptualized
goal orientations as three factors; the other researchers manipulated only mastery and
performance goal orientations.) What varies for each manipulation is the content of the
instructions.

Taking into consideration the various manipulations that researchers have
employed, four general themes appear to emerge: 1) performance is a function of effort
vs. ability (Chambers et al., 2000; Martocchio, 1994; Steele-Johnson et al., 2000); 2)
performance and skills are stable vs. malleable (Chambers et al., 2000; Martocchio, 1994;
Steele-Johnson et al., 2000); 3) avoid making mistakes vs. master the task (Elliot &
Harackiewicz, 1996); and 4) demonstrate what you can do vs. master the task (Ames,
1984; Chambers et al., 2000; Elliot & Harackiewicz, 1996; Elliott & Dweck, 1988;

Jagacinski & Nicholls, 1984; Koestner & Zuckerman, 1994).

19

In addition to task instructions, state goal orientations can be manipulated through
the use of performance feedback (e. g., Butler, 1988) as well as the task environment itself
(e. g., Butler, 1989; Roberts, 1992). For example, performance feedback that could be
used to elicit a state avoid (prove) goal orientation might focus the individual’s attention
on his/her performance relative to others, such as “You answered ﬁve out of ten
questions correctly on the test; most other students answered eight (two) out of ten
correctly.” Conversely, feedback that is intended to induce a state mastery goal
orientation might focus the individual’s attention on his/her performance relative to
him/herself, such as “You answered ﬁve out of ten answers correctly on the test; last
time, you answered three out of ten questions correctly.” In either case, the individual’s
attention is directed towards some reference score, whether the reference score is actually
another individual’s score (as with a prove or avoid orientation) or his/her own score at a
previous time (as with a mastery orientation).

Finally, goal orientation manipulations that use the task environment as a vehicle
often occur naturally. For example, individuals who are active in sports or other group
activities which emphasize winning or being ‘the bes’t’ are continually exposed to a
prove- or avoid-inducing environment (Butler, 1989; Roberts, 1992). Additionally, Ames
(1992) argues that classroom settings are designed in such a way that they actually induce
performance (prove or avoid) goal orientations in most students. Emphasizing standards
such as class rank and honor rolls provide the student with a false sense of what is most
important. That is, rather than urging students to improve their knowledge and skills for
themselves, standards such as those previously mentioned urge students to compete

against one another.

20

Strong needs and desires lea_d to robust mags; As suggested, the strength of an
individual’s needs and desires to behave in a particular manner have direct impact on the
robustness of his/her respective state goal orientations. Further, the strength of these
needs and desires represent themselves in the strength of individuals’ trait goal
orientations (i.e., stronger desires lead to more consistent states, which lead to stronger
traits). Thus, trait goal orientations are representative of the strength of individuals
motivational needs and desires.

Consider the following example: An individual who typically focuses her
attention on mastering the task (i.e., high trait mastery goal orientation) and rarely
focuses on proving her competence (i.e., high trait prove goal orientation) or avoiding the
demonstration of incompetence (i.e., high trait avoid goal orientation) is told that making
mistakes on a given task is a sign of incompetence and such mistakes should be avoided.
If the state manipulation were perfect and the individual’s needs and desires had
absolutely no inﬂuences on state goal orientations, he/she would have a high level of
state avoid goal orientation and low levels of state mastery and prove goal orientations.
This, however, is not necessarily the case; she may instead shift her focus to avoiding
failure rather than mastering the task. Psychologists (e. g., Boyle & Klimoski, 1995; Farr,
Hofmann, & Ringenbach, 1993; Fisher, 1998; Mischel & Shoda, 1995) agree that an
individual’s motivation and behavior in a particular situation result from his/her traits
(representing needs and desires) as well as characteristics of the situation.

Intuitively, the stronger the individual’s traits (i.e., the stronger the individual’s
needs and desires), the more resilient he/she will be to situational inﬂuences. This

resilience to situational inﬂuences translates into more robust state goal orientations.

21

Thus, if his/her needs and desires consistently focus his/her attention on personal
mastery, telling him/her that mistakes are a sign of incompetence will likely prove
useless—hislher state mastery goal orientation will likely be high as well. However, if
his/her needs and desires only sometimes focus his/her attention on personal mastery,
telling him/her that mistakes on the task at hand are a sign of incompetence may lead to a
relatively high state avoid goal orientation.

Current measures of trait goal orientations do not lend themselves well to
measuring the strength or robustness of an individual’s goal orientations (i.e., behavioral
consistency). The most widely used measure of trait goal orientations (i.e., that endorses
a 3-factor conceptualization of goal orientations) is VandeWalle’s (1997) l3-item scale,
and makes no reference to the idea of goal orientation strength. One approach to
determining the strength of an individual’s trait goal orientations would be to use the
magnitude of the scale scores. That is, the more strongly an individual endorses
statements of a given goal orientation, the stronger the orientation. A second approach to
would be to assess the extent to which an individual believes that his/her trait goal
orientation beliefs transfer across situations. That is, the greater the variety of situations
that an individual feels he/she would endorse a particular goal orientation, the stronger
the orientation. As noted, no current measures of trait goal orientation employ the latter
method, and future work should move in this direction. For the current study, however,
the former method provided an adequate measure of trait goal orientation robustness.

The malleability of personality. Thus, far, factors leading to an individual’s state
goal orientations when he/she encounters a new situation or task have been limited to

his/her trait goal orientations and situational inﬂuences or manipulations. Aside from the

22

situation, however, it is quite possible that there are characteristics of the individual that
may moderate the effects of trait goal orientations on state goal orientations. That is,
characteristics of the individual may inﬂuence him/her to adopt state goal orientations
similar to or different from his/her trait goal orientations.

Consider an individual who can adapt to different situations very easily, changing
his/her behavior as necessary. This individual may be more likely to adopt state goal
orientations that are different from his/her trait goal orientations (i.e., more likely to be
inﬂuenced by the situation) compared to an individual who cannot adapt to different
situations as easily. That is, individuals who have very stable personalities and
motivational tendencies are not likely to adopt state goal orientations that are inconsistent
with their trait goal orientations. In contrast, individuals with malleable personalities and
motivational tendencies may very well adopt state goal orientations that are inconsistent
with their trait goal orientations. Although there is no empirical research to support these
propositions, there is also no empirical research suggesting that these propositions are
necessarily incorrect.

Three personality variables that may have implications for behavioral malleability
are openness to experience, agreeableness, and self-monitoring. Although these three
personality characteristics are often discussed in terms of interpersonal interaction, one
could also interpret them as indicators of the malleability of an individual’s behavior;
therefore, they lend themselves to the discussion of trait and state goal orientations.
Thus, as these three personality characteristics increase, we might expect that individuals
are more likely to adopt state goal orientations that are inconsistent with their trait goal

orientations. In contrast, low levels of these personality characteristics may reﬂect

23

stability and consistency in individuals’ behavior, leading an individual to adopt state
goal orientations that are similar to their trait goal orientations.

Openness to experience, as its name implies, refers to an individual’s appreciation
for change and new experiences (McCrae & Costa, 1985). As Barrick and Mount (1991)
note, Openness to experience is also commonly associated with broad-mindedness. Thus,
an individual who has a high level of openness to experience, by deﬁnition, is easily
inﬂuenced by situations—he/she has an open mind regarding different situations.

A second personality characteristic, agreeableness, also has implications for the
stability or malleability of an individual’s behavior. Agreeableness refers to the extent to
which an individual is cooperative, tolerant, and ﬂexible (Barrick & Mount, 1991;
McCrae & Costa, 1985). Consider an individual who has a low level of agreeableness.
This individual is expected to be adamant in his/her views and opinions. As an extension
of this logic, one could also expect such an individual to be set in his/her behaviors as
well.

Finally, self-monitoring refers to the extent to which an individual is sensitive to
situational cues of appropriateness and regulates his/her behavior accordingly (Snyder,
1974). Again, self-monitoring has implications for stable vs. malleable behavior.
Speciﬁcally, an individual with a high level of self-monitoring ﬁnds it easy to adapt
his/her behavior to ﬁt the situation. Thus, if situational cues indicate to the individual
that he/she should focus on mastering a task (i.e., mastery goal orientation), he/she will
likely focus on mastering the task at hand.

As discussed in the Methods section of this paper, subjects in the current study

engaged in a problem-solving task. At various points throughout the experiment,

24

individuals were asked to describe their motivation and specify the nature of their goals.
In such a situation, goal orientations are highly salient and could be expected to vary as a
function of time. Other characteristics of their personality (i.e., such as openness to
openness to experience, agreeableness, and self-monitoring), however, are not as likely to
change over time. If, for example, individuals were asked to engage in a variety of social
situations rather than a problem-solving task, one might expect that the aforementioned
personality variables would be more likely to change over time compared to goal
orientations. In the context of the current experiment, however, these personality
variables were not expected to vary. For this reason, a distinction is made between trait
and state goal orientations, but this distinction is not made for openness to experience,
agreeableness, or self-monitoring.

Inﬂuence of self-efﬁcacy on state goal orientations. Bandura (1997) deﬁnes self-
efﬁcacy as “beliefs in one’s capabilities to organize and execute the courses of action
required to produce given attainments” (p. 3). In essence, self-efﬁcacy refers to an
individual’s perceived ability to accomplish a given task or goal. Given this deﬁnition, it
should come as no surprise that researchers (e.g., Zimmermann & Bandura, 1994;
Zimmermann, Bandura, & Martinez-Pons, 1992) have argued that self-efﬁcacy beliefs
actually govern behavioral accomplishments.

The method by which self-efﬁcacy beliefs dictate behavioral success can best be
understood in terms of effort expenditure and task persistence. As Bandura (1997)
explains, an individual who has low self-efﬁcacy in a speciﬁc situation will ﬁnd it
difﬁcult to motivate him/herself, thus reducing the amount of effort that he/she expends.

This diminished effort expenditure translates into decreased performance. An individual

25

with high self-efﬁcacy in a given situation, in contrast, is not likely to experience the
same diminished motivation, effort, and performance. Instead, he/she will likely persist
in the face of difﬁculty and continue to exert the effort necessary for success. Indeed,
Locke and Latham (1990) argue that self-efﬁcacy leads to subsequent motivation, goals,
and performance (see links 4-6 in Figure 1).

This discussion of effort expenditure and task persistence is highly similar to the
previous discussions of goal orientations. Speciﬁcally, it was noted that an individual
with a high level of (state) mastery goal orientation is motivated by the desire to learn and
master skill or task at hand; such an individual is likely to increase effort and persist in
the face of difﬁculty (VandeWalle, 1997). In contrast, an individual with a high level of
(state) avoid goal orientation is motivated in large part by the desire to avoid appearing
incompetent; such an individual is likely to withdraw from the task at hand if failure is
probable (VandeWalle, 1997). Thus, mastery and avoid goal orientations correspond
nicely to individuals with high and low self-efﬁcacy (respectively), as described by
Bandura (1997).

Although Bandura’s discussions of self-efﬁcacy make no reference (explicit or
implicit) to prove goal orientation, the reader is reminded that the key motivating factor
of this kind of goal orientation is the demonstration of one’s competence to others
(V andeWalle, 1997). Therefore, an individual with a high level of (state) prove goal
orientation is motivated (and exerts effort) in situations where he/she believes that
success is probable. By deﬁnition, such a belief is present when self-efﬁcacy is high.

To this point, the antecedents of self-efﬁcacy have not been addressed explicitly.

This issue is addressed in a later section.

26

Summaﬂ of state and trait goal orientations. Thus far, it has been demonstrated
that individuals’ state goal orientations are not predicted solely by their trait goal
orientations. Instead, an individual’s trait goal orientations and situational factors (e.g., a
goal orientation manipulation) jointly inﬂuence his/her state goal orientations. Further,
this relationship between trait goal orientations, situational factors, and state goal
orientations is interactive in nature, and the robustness of state goal orientations is
dependent upon the strength of trait goal orientations (see links 2 and 3 in Figure 1).
Speciﬁcally, the stronger an individual’s trait goal orientations, the less resilient he/she
will be to situational inﬂuences and the more robust will be his/her respective state goal
orientations.

In addition to the interaction between trait goal orientations and situational
inﬂuences, it was suggested that characteristics reﬂecting the stability and consistency of
an individual’s behavior (i.e., openness to experiences, agreeableness, self-monitoring)
may interact with trait goal orientations in the detemrination of state goal orientations.
Finally, it was shown that an individual’s self-efﬁcacy in a given situation inﬂuences
his/her state goal orientations (see links 4 and 16 in Figure 1 for an illustration of these
relationships).

Now that the antecedents of an individual’s initial state goal orientations for a
given situation have been identiﬁed and discussed, attention shifts to the dynamic
processes involved in the stability of state goal orientations over time. In doing so, we
examine the roles of performance feedback, performance evaluations, and affect. First,

however, we must address the process by which performance feedback becomes salient.

27

Self-Set Goals Performance and Feedback

 

The premise of this section is simple: for every situation, an individual sets goals
for him/herself; these goals translate into behavior and subsequent performance; and the
individual receives (internal or external) feedback regarding his/her performance (see
links 6 and 7 in Figure 1). The current section examines each stage in this process in

more detail.

 

Goal setting leads to performance, and mrformance leads to feedback. Locke and

Latham (1990, p. 2) describe a goal as “the generic concept that encompasses the
essential meaning of terms such as intention, task, deadline, purpose, aim, end, and
objective. . .all of these have in common the element that there is something that the
person wants to achieve.” Thus, goal setting can be described as the process whereby an
individual chooses a particular (desired) end-state toward which he/she works.

Goal setting, essentially, is the driving force of behavior—it is the mechanism by
which motivation leads to action. Intuitively, this action then leads to performance; the
speciﬁc nature of this performance (i.e., good or bad), however, is of little importance.
Instead, understanding that there exists a relationship between the goals that individuals
set for themselves (i.e., self-set goals) and subsequent performance is crucial for
understanding goal setting theory.

In a meta-analysis of 13 studies (and 2,285 subjects) from 1973-1989, Locke and
Latham (1990) found an average correlation (weighted by sample size) between self-set
goals and subsequent performance of .42; observed correlations in the individual studies
ranged from .16-.83. Thus, higher self-set goals lead to higher performance, and vice

V6138.

28

Internal and external sources of feedb§c_k. After an individual performs a task,
he/she receives some sort of feedback regarding his/her performance. This feedback can
come either as the result of another individual telling him/her “You did well/poorly,” or it
can come from within the individual. That is, an individual may evaluate his/her own
performance on a task and come to the conclusion that “I did pretty well/poorly.” In any
situation, then, an individual can always gather some form of feedback. (Just because an
individual can obtain feedback regarding his/her performance, this does not necessarily
mean that he/she will obtain feedback.)

Based on the discussion thus far, it is evident that the goals an individual sets for
him/herself have direct impact on his/her performance, and this performance translates
into some form of feedback—often positive or negative in nature. What have not been
addressed thus far are the factors that lead an individual to choose a goal. Attention now
shifts in this direction.

How does an indiviﬂl choose agLal? Factors leading to goal choice (i.e., self-
set goals) are numerous, but two of the most robust factors are self-efﬁcacy and valence.
Recall from above that self-efﬁcacy refers to the “beliefs in one’s capabilities to organize
and execute the courses of action required to produce given attainments” (Bandura, 1997,
p. 3). Valence, in contrast, refers to the value that an individual places on goal attainment
(Locke & Latham, 1990). Based on these two factors alone, (Locke & Latham, 1990)
note that researchers (e.g., Dachler & Mobley, 1973; Reidel, Nebeker, & Cooper, 1988)
have been able to predict goal choice successfully.

Although the proposed conceptual model (see Figure 1) does not contend that

goal setting is the direct result of self-efﬁcacy and valence, these relationships still exist

29

in the model. The current model proposes that an individual’s state goal orientations lead
directly to goal choice. Moreover, one of the components affecting state goal orientation
is self-efﬁcacy. Thus, the current model proposes that the link between self-efﬁcacy and
goal setting is mediated by state goal orientations.

Further, the concept of goal valence is embedded in state goal orientations.
Recall from discussion of goal orientations that individuals can have varying levels or
strengths of speciﬁc (state) goal orientations. Thus, the more important the attainment of
a particular goal is to the individual, the more centrally motivating is that goal. This
increased importance or centrality of a particular goal, then, is translated into stronger
goal orientations.

To this point, factors leading to an individual’s state goal orientations have been
examined, and the process by which these state goal orientations lead to subsequent
goals, performance, and feedback has been discussed. How an individual reacts to this
feedback becomes an important question when we are interested in predicting his/her
subsequent motivation. In the following sections, the (moderated) effects of feedback on
performance evaluations and affect are explored.

Performance Evaluations

Weiner’s contributions. Over the last three decades, the work of Weiner has
inﬂuenced the attribution literature to a great extent, speciﬁcally regarding attributions
for achievement outcomes. In discussing the possible attributions that individuals can
make following achievement outcomes, Weiner (1979) highlights three independent

dimensions of attributions: locus, stability, and controllability.

30

The locus of an event refers to the extent to which an individual believes that
his/her performance was within his/her boundaries of control—cg, “Did my teacher
sabotage my work, or did I just not do well?” The stability of an event refers to the
extent to which an individual believes that a similar outcome will occur in the future—
e.g., “Was this a one-time event, or is it possible that may fail a math test in the future?”
Finally, the controllability of an event refers to the extent to which an individual believes
that he/she can control similar events in the future—cg, “Can I study harder next time
and do better, or am I doomed to failure?” Based on these three dimensions, individuals
can make vastly different attributions for their performance.

Success vs. failure. In later work, Weiner (1986) noted that before an individual
makes any attributions as described above, he/she ﬁrst makes an overall evaluation of
his/her performance. Speciﬁcally, individuals decide whether they were successful or
unsuccessful on the task. This success-failure evaluation then initiates both affective
reactions to the task (i.e., feedback) as well as the more speciﬁc attribution process.

Although Weiner does not address this issue, whether an individual concludes that
he/she succeeded or failed at a task is not as straightforward as simply knowing whether
he/she received positive or negative performance feedback. Instead, different individuals
will interpret positive and negative feedback differently. Take as an example an
individual who receives negative performance feedback. He/she can interpret this
feedback in one of two ways. First, he/she can conclude that the negative feedback must
mean that he/she has failed the task. However, he/she can also make the less severe

conclusion that he/she was not as successful as he/she could have been.

31

The goal orientation literature suggests that, compared to high levels of state
mastery goal orientation, high levels of state avoid goal orientation motivate individuals
to underestimate their number of successes, overestimate their number of failures, and to
view current success as unpredictive of future success—they view their successes (i.e.,
positive feedback) as less successful (Diener & Dweck, 1980). Essentially, individuals
who focus a great deal of attention on avoiding failure and relatively little attention on
learning the task (i.e., high levels of avoid goal orientation and low levels of mastery goal
orientation) make negative performance evaluations. In contrast, individuals who focus a
great deal of attention on learning the task and relatively little attention on avoiding
failure (i.e., high levels of mastery goal orientation and low levels of avoid goal
orientation) tend to make more positive—yet realistic—performance evaluations.
Further, because high levels of prove goal orientation motivate an individual to focus on
attaining success (i.e., the opposite effect of high levels of avoid goal orientation), one
could infer that high levels prove goal orientation would motivate individuals to interpret
positive and negative feedback in positive—and possibly unrealistic—manner.

State and trait goal orientations and mrformance evaluations. As noted, an
individual may have different state and trait goal orientations in a given situation.
Whether an individual uses his/her trait or state goal orientations to interpret performance
feedback, however, is a question that has yet to be addressed in the literature. For
example, one could argue that because we are talking about an individual behaving in a
speciﬁc situation, he/she will interpret feedback based on his/her state goal orientations.
However, one could also argue that just because an individual may be motivated by state

goal orientations that are different from his/her trait goal orientations in a speciﬁc

32

situation, this does not mean that his/her trait goal orientation remain mute in the
feedback interpretation process. Instead, one could infer that an individual uses both
his/her state goal orientations as well as his/her trait goal orientations to interpret
feedback (see links 9 and 11 in Figure l).

Summm of performance evaluations. In this section it has been demonstrated
that the evaluations individuals make regarding their performance depend in large part on
their goal orientations. Speciﬁcally, high levels of mastery and prove goal orientations
are likely to motivate individuals to be more positive in their performance evaluations,
whereas high levels of avoid goal orientation are likely to motivate individuals to be more
negative in their performance evaluations. Further, it was suggested that high levels of
prove and avoid goal orientations are likely to motivate individuals to make unrealistic
performance evaluations, whereas high levels of mastery goal orientation are likely to
motivate individuals to make more realistic performance evaluations.

In addition, the arguments presented in this section have suggested that
individuals’ state and trait goal orientations inﬂuence the evaluations that individuals
make regarding their performance (see links 9 and 11 in Figure 1 for a summary of these
relationships). In the next section, attention shifts toward the effects of feedback on
affective reactions.

Affective Reigtions to Feedbﬁ

In the discussion of control theory, it was proposed that individuals are motivated
by one of two discrepancy loops: a discrepancy reducing loop—whereby individuals are
motivated to work toward some positively valenced goal—and a discrepancy enlarging

loop—whereby individuals are motivated to move away from some negatively valenced

33

goal (Carver et al., 1996; Carver & Scheier, 1995). Not only do individuals who engage
in these two loops differ in their motivation, but they also differ in the affect that they
experience following performance.

Following the lead of Higgins (1987; Higgins et al., 1986), Carver and his
associates (Carver et al., 1996; Carver & Scheier, 1995) proposed that individuals who
engage in discrepancy reducing loops tend to experience affect in terms of joy, ranging
from elation to depression. That is, individuals become depressed when they fail to
achieve some reward. In contrast, individuals who engage in discrepancy enlarging loops
tend to experience affect in terms of distress, ranging from relief to anxiety (Carver et al.,
1996; Carver & Scheier, 1995). That is, individuals become anxious when they fail to
avoid some punishment.

Whether an individual experiences high or low levels of joy (or distress)
following performance is dependent on their perceived performance. That is, if the
individual feels that he/she did well, elation (or relief) will result. In contrast, if the
individual feels that he/she did not do well, depression (or anxiety) will result.

State and trait goal orientations and affective reactions. Parallels were drawn
between those who engage in discrepancy reducing loops and mastery and prove goal
orientations, and between those who engage in discrepancy enlarging loops and avoid
goal orientation. Further, as noted in the discussion of performance evaluations, positive
and negative performance feedback do not necessarily lead to perceptions of doing well
and doing poorly, respectively. Instead, individuals’ levels of goal orientations (i.e., state
and trait) inﬂuence the extent to which they make success and failure evaluations (see

links 9 and 11 in Figure 1). Recall that high levels of avoid goal orientation are likely to

34

motivate individuals to interpret negative feedback as failure, whereas high levels of
prove and mastery goal orientations are likely to motivate individuals to interpret
negative feedback less severely.

Finally, research suggests that high levels of mastery goal orientation are
associated with the experience of less extreme levels of affect compared to the affect
associated with high levels of prove and avoid goal orientations. For example, Steele-
Johnson et a1. (2000) found that high levels of mastery goal orientation were associated
with only moderate levels of satisfaction/dissatisfaction regarding performance on a
problem-solving task. In contrast, high levels of performance goal orientation (these
researchers did not distinguish between performance-avoid and performance-prove) were
associated with extreme levels of satisfaction/dissatisfaction. Thus, goal orientations
(i.e., state and trait) moderate the effects of performance feedback sign on affective
reactions (see links 10 and 12 in Figure 1).

Performance evaluations and affect. Irnbedded in the discussion of goal
orientations and affect is the notion of a temporal relationship between individuals’
performance evaluations and affect. Speciﬁcally, it is quite possible that performance
evaluations inﬂuence individuals’ affect. If an individual feels that he/she was successful
or that effort on the task is useful, he/she is likely to experience higher levels of affect
(i.e., elation or relief). In contrast, if an individual feels that he/she failed or that effort on
the task is useless, he/she is likely to experience lower levels of affect (i.e., depression or
anxiety). Indeed, Weiner (1986) notes that individuals respond to task performance (i.e.,
feedback) by making a general evaluation regarding their performance, which then leads

to affective reactions and more speciﬁc attributions (see link 13 in Figure 1).

35

Su_mm_arv of affective rea_ctions to feedbzglg In this section, it was demonstrated
that high levels of prove and mastery goal orientations (i.e., individuals who engage in
discrepancy reducing loops) are associated with affective reactions along a joy dimension
(ranging from elation to depression). In contrast, high levels of avoid goal orientation
(i.e., individuals who engage in discrepancy enlarging loops) are associated with affective
reactions along a distress dimension (ranging from relief to anxiety).

In addition, the moderating effects of state and trait goal orientations were
identiﬁed, such that positive and negative feedback lead to extreme levels of elation (or
relief) and depression (or anxiety) when coupled with high levels of prove (or avoid) goal
orientation. In contrast, positive and negative feedback lead to less extreme levels of
elation and depression when coupled with high levels of mastery goal orientation. These
effects were discussed in terms of a temporal relationship between performance
evaluations and affect.

In the next section, the dynamics of individuals’ state goal orientations are
examined in terms of the constructs discussed thus far. Speciﬁcally, the roles of
performance evaluations, self—efﬁcacy, and affect in the dynamic processes behind state
goal orientations are explored.

State Goal Orientations Over Time

Every time an individual performs a task and receives some sort of feedback
regarding his/her performance (whether the feedback is internally generated or externally
offered is irrelevant), the situation changes. No longer does the individual necessarily
operate under the same set of assumptions regarding the task; nolonger does the

individual necessarily have the same sense of self-efﬁcacy regarding the task; and no

36

longer does the individual necessarily experience the same affect when thinking about the
task. Thus, because all of these factors of the situation can change, it is only reasonable
to conclude that an individual’s state goal orientations can change as well (note the cycle
nature of Figure 1, represented by a dotted line).

Self-efﬁcacy revisited. In an earlier section, it was noted that the effects of high
levels of self-efﬁcacy resemble the effect of high levels of mastery and prove goal
orientations and that the effects of low levels of self—efﬁcacy resemble those of high
levels of avoid goal orientation. Further, Locke and Latham (1990) argued that self-
efﬁcacy leads to subsequent goals, motivation, and performance. Thus, the proposition
was offered that an individual’s self-efﬁcacy in a given situation inﬂuences his/her state
goal orientations (see links 4 and 16 in Figure 1). An important question that arises, then,
is ‘What inﬂuences self-efﬁcacy in a given situation?’

Researchers (e.g., Fisher, 1998; Locke & Latham, 1990; Mone & Baker, 1992)
have argued that self-efﬁcacy is determined in large part by past experience with a task.
For example, Fisher (1998) found that individuals’ goal-performance discrepancies in a
college classroom setting signiﬁcantly predicted subsequent self-efﬁcacy. Thus, these
ﬁndings suggest that individuals make some sort of conscious evaluation of their
performance (e. g., based on performance-goal discrepancies), and these evaluations lead
to subsequent self-efﬁcacy. Speciﬁcally, when an individual feels that his/her
performance on a task was poor, subsequent self-efﬁcacy is low—the individual has low
conﬁdence in his/her ability based on past experience. When an individual feels that
his/her performance was good, subsequent self—efﬁcacy is high—the individual has high

conﬁdence in his/her ability based on past performance.

37

Affect revisited. The ﬁnal link in Figure 1 that has yet to be explained is between
affect and subsequent state goal orientations (see link 14). The consensus in the goal and
affect literatures is that affect leads to subsequent motivation, which is usually
operationalized as effort expenditure or persistence (for a review, see Schwarz & Bohner,
1996). For example, the literature argues that positive affect (e. g., satisfaction) leads to
high effort exertion and goal persistence and that negative affect (e. g., dissatisfaction)
leads to low effort exertion and goal persistence. That is, negative affect signals to the
individual that success is not likely and that he/she should disengage from the task
(Schwarz & Bohner, 1996).

This explanation, however, does not lend itself well to the discussion of state goal
orientations—where individuals are motivated by different factors. Speciﬁcally, the
literature does not consider the possibility that different levels of affect (and different
kinds of affect) may lead to different kinds of goals. For example, an individual with a
high level of state prove goal orientation (and low levels of state mastery and avoid goal
orientations) who experiences negative feedback may adopt a higher level of state avoid
goal orientation in the future. That is, rather than focus on the demonstration of ability,
this individual may shift his/her focus to avoiding the demonstration of incompetence.
One such way to do so may be to reduce effort and focus on some other aspect of the
task. That way, the individual can respond to negative feedback by thinking ‘I did not
perform well because I did not try’ as opposed to thinking ‘I tried real hard and I still did
not perform well. . .I must be stupid.’

Regardless, it is evident that an individual’s affective reactions to an event have

impact on the goals that he/she adopts as well as his/her future effort and persistence.

38

Now that the relevant theory and literature have been discussed regarding each
component in the conceptual model (see Figure 1), an operational mode] and a series of
testable hypotheses are offered.

Research Hymtheses

Figure 2 contains an operational model for the questions of interest in the current
study. Compared to the conceptual model (i.e., Figure l), the only differences between
the two models are l) greater speciﬁcity in Figure 2 regarding the operationalization of
particular variables, and 2) the elimination of goal setting and performance from the
operational model. Whereas the former difference is reasonable, the latter difference
deserves some discussion.

In the current study, speciﬁc goals were assigned to individuals, thus eliminating
the goal setting process from consideration. Further, the interest in the current study was
not on individuals’ performance per se. Rather, interest resided in individuals’
interpretations of and reactions to their performance (i.e., performance feedback). That
is, how an individual evaluates his/her performance based on feedback and how he/she
reacts affectively to this feedback was of primary interest in the current study. Thus,
performance served simply as a vehicle for the delivery of believable feedback. For these
reasons, goal setting and performance were eliminated from the operational model and
were not examined explicitly.

Factors leading to initial state goal orientations. Recall the previous example of
an individual with high trait mastery goal orientation and low trait prove and avoid goal
orientations. If told that making mistakes on a given task was a sign of incompetence and

that such mistakes should be avoided at all costs (i.e., an avoid manipulation), this

39

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40

individual is likely to continue to be motivated by high levels of state mastery goal
orientation and not believe the instructions. In contrast, if this individual’s trait mastery
had been lower, such instructions would have been more successful in convincing

him/her to adopt avoid—oriented goals. Thus,

Hypothesis 1: Trait goal orientations and a goal orientation manipulation would
interact in their eﬁects on initial state goal orientations. Specifically, as a
particular trait goal orientation increases, the inﬂuence of an inconsistent goal

orientation manipulation on the respective state goal orientation will decrease.

In addition to the joint effects of trait goal orientations and a goal orientation
manipulation, attention was drawn to the moderating effects of openness to experiences,
agreeableness, and self-monitoring on state goal orientations. Speciﬁcally, these three
personality characteristics were identiﬁed as measures of an individual’s stability and
consistency in terms of motivation and behavior. As an individual’s desire for
consistency and stability of behavior increases, he/she is likely to adopt state goal
orientations that are consistent with his/her trait goal orientations. Put another way, as
openness to experiences, agreeableness, and self-monitoring increase, an individual is
more likely to adopt state goal orientations that are inconsistent with his/her trait goal

orientations. Thus,

Hypothesis 2: Trait goal orientations and individual diﬁ‘erence variables would
interact in their effects on initial state goal orientations. Specifically, as openness
to experiences, agreeableness, and self-monitoring increase, the relationship
between trait goal orientations and respective state goal orientations would
decrease.

Finally, Bandura (1997) notes that an individual with low self-efﬁcacy in a
speciﬁc situation will ﬁnd it difﬁcult to motivate him/herself, thus reducing the amount

of effort that he/she expends. In contrast, an individual with high self-efﬁcacy in the

41

same situation is likely to exert much effort so as to succeed (Bandura, 1997). Parallels
were drawn between individuals with high self—efﬁcacy and those with high levels of
state mastery and prove goal orientations as well as between individuals with low self-
efﬁcacy and those with high levels of state avoid goal orientation. Further, the temporal
relationship that exists between these variables is one whereby self-efﬁcacy leads to
motivation (Locke & Latham, 1990). Thus,

Hypothesis 3: Self-efﬁcacy would have a main effect on initial state goal
orientations.

Speciﬁcally,

Hypothesis 3a: As self-eﬂicacy increases, initial state mastery goal orientation
would increase.

Hypothesis 3b: As self-eﬁ‘icacy increases, initial state prove goal orientation
would increase.

Hypothesis 3c: As self-efﬁcacy decreases, initial state avoid goal orientation
would increase.

Effects of goal orientations and feedback on performance evaluations. In the
discussion of success vs. failure performance evaluations, it was noted that high levels of
mastery and prove goal orientations tend to be associated with more positive performance
evaluations whereas high levels of avoid goal orientation tend to motivate individuals to
make more negative performance evaluations. In addition, it was noted that both an

individual’s state and trait goal orientations play integral roles in this process. Thus,

Hypothesis 4: State goal orientations and feedback sign (positive vs. negative)

would interact in their eﬁects on performance evaluations.

Speciﬁcally,

42

Hypothesis 4a: As state mastery goal orientation increases, positive feedback
would lead to performance evaluations of success and negative feedback would

lead to performance evaluations of neither success nor failure.

Hypothesis 4b: As state prove goal orientation increases, positive feedback
would lead to performance evaluations of success and negative feedback would
lead to performance evaluations of failure.

Hypothesis 40: As state avoid goal orientation increases, positive feedback would
lead to performance evaluations of success and negative feedback would lead to

performance evaluations of failure.

In addition,

Hypothesis 5: Trait goal orientations and feedback sign (positive vs. negative)

would interact in their effects on performance evaluations.

Speciﬁcally,

Hypothesis 53: As trait mastery goal orientation increases, positive feedback
would lead to performance evaluations of success and negative feedback would

lead to performance evaluations of neither success nor failure.

Hypothesis 5b: As trait prove goal orientation increases, positive feedback would
lead to performance evaluations of success and negative feedback would lead to

performance evaluations of failure.

Hypothesis 5c: As trait avoid goal orientation increases, positive feedback would
lead to performance evaluations of success and negative feedback would lead to

performance evaluations of failure.

Effects of goal orientations and feedback on affect. Parallels were drawn between
engaging in discrepancy reducing loops and mastery and prove goal orientations, and
between engaging in discrepancy enlarging loops and avoid goal orientation. In addition,
research suggests that the affect that individuals experience differs for discrepancy

reducing and enlarging loops. Speciﬁcally, discrepancy reducing loops lead to the

43

experience of joy (elation vs. depression) and discrepancy enlarging loops lead to the
experience of distress (relief vs. anxiety) (Carver et al., 1996; Carver & Scheier, 1995).
Further, it was noted that high levels of mastery goal orientation tend to be associated
with less extreme levels of affect compared to the affect associated with high levels of
prove and avoid goal orientations (Steele-Johnson et al., 2000). Finally, similar to the
relationship between goal orientation, feedback sign, and performance evaluations, it was
suggested that an individual’s trait and state goal orientations are integral in the

experience of affect. Thus,

Hypothesis 6: State goal orientations and feedback sign (positive vs. negative)
would interact in their eﬂ'ects on the experience of affect.

Speciﬁcally,

Hypothesis 6a: As state mastery goal orientation increases, positive feedback
would lead to positive levels of joy ( i. e., elation) and negative feedback would
lead to negative levels of joy (i. e., depression).

Hypothesis 6b: As state prove goal orientation increases, positive feedback
would lead to positive levels of joy ( i. e., elation) and negative feedback would
lead to negative levels of joy (i.e., depression).

Hypothesis 6c: As state avoid goal orientation increases, positive feedback would
lead to positive levels of distress (i. e., relief) and negative feedback would lead to

negative levels of distress (i.e., anxiety).

In addition,

Hypothesis 7: Trait goal orientations and feedback sign (positive vs. negative)
would interact in their effects on the experience of affect.

Speciﬁcally,

Hypothesis 73: As trait mastery goal orientation increases, positive feedback
would lead to positive levels of joy ( i. e., elation) and negative feedback would

lead to negative levels of joy ( i.e., depression).

Hypothesis 7b: As trait prove goal orientation increases, positive feedback would
lead to positive levels of joy (i. e., elation) and negative feedback would lead to

negative levels of joy (i.e., depression).

Hypothesis 7c: As trait avoid goal orientation increases, positive feedback would
lead to positive levels of distress (i. e., relief) and negative feedback would lead to

negative levels of distress (i.e., anxiety).

Dmamic processes behind state goal orientations. Based on Weiner’s (1986)

argument that individuals, after performing a task and receiving some sort of feedback
(internal or external) regarding their performance, make general evaluations of their

performance followed by affective reactions to those evaluations. Thus,

Hypothesis 8: An individual ’s performance evaluation would have a main effect
on the experience of affect. Specifically, evaluations of success would lead to
positive levels of joy (i.e., elation) and distress (i. e., relief) and evaluations of
failure would lead to negative levels of joy ( i. e., depression) and distress (i.e.,

anxiety).

As noted, researchers have argued that self-efﬁcacy is determined in large part by
past experience with a task. Speciﬁcally, ‘good’ performance at a task leads one to have
a high sense of perceived capability (i.e, self-efﬁcacy) regarding the task at a later time,
whereas ‘poor’ performance leads to low perceived capability (Fisher, 1998; Locke &

Latham, 1990; Mone & Baker, 1992). In line with these ﬁndings, then,

Hypothesis 9: An individual '3 performance evaluation would have a main effect
on subsequent self-efficacy. Speciﬁcally, evaluations of success would lead to
high self-efficacy and evaluations of failure would lead to low self-efficacy.

45

Recall that Hypothesis 3 predicted that individuals’ levels of self-efﬁcacy would
lead to initial state goal orientations (see Figure 2). Placing state goal orientations in a

dynamic context, then, this hypothesis generalizes to subsequent state goal orientations as

well. Thus,

Hypothesis 10: Self-efficacy would have a main eﬁect on subsequent state goal
orientations.

Speciﬁcally,

Hypothesis 103: High self-efﬁcacy would lead to high levels of subsequent state
mastery goal orientation.

Hypothesis 10b: High self-eﬂicacy would lead to high levels of subsequent state

prove goal orientation.

Hypothesis 10c: Low self-efficacy would lead to high levels of subsequent state

avoid goal orientation.

Finally, as suggested in the discussion of affective reactions to feedback, high
levels of state mastery and prove goal orientations lead to different kinds of affect
compared to high levels of state avoid goal orientation. Speciﬁcally, high levels of state
mastery and prove goal orientations lead to the experience of joy (elation vs. depression),
whereas high levels of state avoid goal orientation lead to the experience of distress
(relief vs. anxiety). Further, it was noted that high levels of mastery goal orientation tend
to be associated with less extreme affective experiences compared to the affective
experiences associated with high levels of prove and avoid goal orientations.

Although the goal orientation literature does not address this issue, it was
suggested that affect has direct inﬂuence on subsequent motivation (Schwarz & Bohner,
1996). However, the nature of this inﬂuence in terms of goal orientation is unclear. One

possibility for the nature of this inﬂuence could be that affect serves as a reinforcement

46

for state goal orientations. For instance, because high levels of prove and avoid goal
orientations are associated with extreme levels of affect, one could infer that extreme
levels of affect lead to high levels of state prove and avoid goal orientations.
Speciﬁcally, extremely positive affect (i.e., elation or relief) might lead an individual to
seek that positive affect in the future, thus adopting a high level of state prove goal
orientation. In contrast, extremely negative affect (i.e., depression or anxiety) might lead
an individual to avoid that negative affect in the future, thus adopting a high level of state
avoid goal orientation. Finally, because high levels of mastery goal orientation are
associated with moderate levels of affect, one could infer that moderate levels of affect
lead to high levels of state mastery goal orientation. Thus,

Hypothesis 11: Affect would have an effect on subsequent state goal orientations.

Speciﬁcally,

Hypothesis lla: As the experience of distress becomes more positive (i. e., relief),
subsequent state prove goal orientation would increase. As the experience of
distress becomes more negative (i. e., anxiety), subsequent state avoid goal

orientation would increase.

Hypothesis 11b: As the experience of joy becomes more positive (i. e., elation),
subsequent state mastery and prove goal orientations would increase. As the
experience of joy becomes more negative (i. e., depression), subsequent state

avoid goal orientation would increase.

The dotted lines in Figures 1 and 2 represent the cyclical nature of the models.
That is, data in the current study were collected in waves, and the majority of the research
hypothesis (i.e., Hypotheses 4-11) were relevant and needed to be tested beyond the ﬁrst
wave. The manner in which these analyses were conducted is discussed in the Results

section of this paper.

47

METHOD
Study Desigp

A 3 (mastery vs. prove vs. avoid goal orientation manipulation) X 2 (positive vs.
negative feedback) X 3 (trial blocks) design was used; each block contained 15 trials. All
procedures were completed via computer.

MSG—ts

Three hundred thirteen undergraduate students participated as subjects in the
current experiment in exchange for research participation points to be applied toward
their introductory psychology class. Three subjects were removed from analyses because
they did not ﬁnish the experiment. Of the 310 subjects in the ﬁnal sample, 59% were
female, 46% were freshmen, and their mean age was 19.6 years (S_D = 2.03 years). On
average, subjects reported that they did not know much about the stock market M = 3.91,
g; = .82) in response to the question “How much do you know about the stock market?”
(1 = “Very much”, 5 = “Nothing at all”).

Prior to the study, a power analysis was conducted to determine a sufﬁcient
sample size. Rather than identify the sample size needed to obtain a particular 'level of
power given an alpha level and effect size, the percentage of variance needed to be
explained by each of the hypothesized effects given a level of power and sample size was
computed (Murphy & Myors, 1998). Based on a sample of 300 (i.e., approximately 50
per condition), a power analysis revealed that the hypothesized effects would need to
account for approximately l.5—2% of the variance in order to achieve power = .5.
Approximately 2.5-3% of the variance in the dependent variables would need to be

explained by the hypothesized effects in order to achieve power = .8. These effect sizes

48

are comparable to Cohen’s (1992) small-medium effects. Thus, a sample of 310 yielded
sufﬁcient power in the current study.
Lag Overview_ and Criterion Measure

A multiple cue probability learning (MCPL) task was used as the performance
task in the current study. In this task, subjects were asked to assume the role of a stock
broker who had to predict the closing price of some stocks. Subjects were given
information regarding ﬁve factors that affect stock price (i.e., number of outstanding
shares; yearly earnings for the previous year, projected yearly earnings for the current
year, total assets, and total liabilities), and they were instructed to ignore any prior
knowledge of stock prices. The subjects’ task was to predict the closing price of each
stock based on the values provided for each of the inﬂuential factors. (See Appendix A
for a sample trial.)

The negative consequences of overestimating stock prices (i.e., opportunity cost
for holding the stock too long—loss of money) and underestimating stock prices (i.e., sell
the stock too soon—loss of money) were emphasized so that subjects would not
purposely over-predict or under-predict stock prices. Speciﬁcally, subjects were told
that,

“If a stock broker overestimates the closing price of a stock, people may hold onto
their stock for too long because they think it will be worth more later—in this
case they would lose money because they should have sold the bad stock and
bought a better one. If the stock broker underestimates the closing price of a
stock, people may sell the stock too early—in this case they would also lose
money because they sold the stock too soon. In either case, the investor would

lose money because the stock broker’s prediction was not accurate.”

49

‘Actual’ stock prices were computed using a complex algorithm that was
developed for this study. After each trial, subjects were given the actual stock price and
were reminded of their prediction. The absolute difference between actual and predicted
stock price was calculated for each trial. These difference scores were averaged across
the 15 trials in each block (only 14 trials in Block 1 were averaged because Trial 1 was
used to acclimate subjects to the task) to obtain an ‘average error score’ for each subject.
These average error scores were used to provide subjects with normative feedback, as
discussed below.

Goal Orientation Manipulations

As noted above, researchers have used a variety of task instructions to manipulate
individuals’ goal orientations. Further, these different manipulations have tended to
center around four general themes: 1) performance is a function of effort vs. ability; 2)
performance and skills are stable vs. malleable; 3) avoid making mistakes vs. master the
task; and 4) demonstrate what you can do vs. master the task. Goal orientations were
manipulated in the current study through the use of task instructions based on these four
themes. Instructions used to elicit a high level of state mastery goal orientation focused
the individual’s attention on the perception that performance on the task could be
improved with practice (i.e., performance was a function of effort) and that one could

learn from his/her mistakes:

“Successful performance on this task requires the use of skills that must be
developed and practiced. At ﬁrst, you should expect to make a lot of
mistakes, but this is ok because we can learn from our mistakes. Through
practice, though, you will be able to develop the necessary skills increase
your performance. It is important for you to remember that the more
practice you have, the better you will be able to perform on this task.

50

Thus, you should focus your attention on developing your skills and
mastering the task rather than on the mistakes that you make.”

Instructions used to elicit a high level of state prove goal orientation focused the
individual’s attention on the perception that performance on the task could not be
improved with practice and that performance was based on the individual’s underlying
abilities. In addition, the individual’s attention was directed toward demonstrating

his/her competence to others:

“Successful performance on this task is based on skills and abilities that
either you do or do not already have. We are interested in comparing your
performance on this task to that of other individuals so that we can
examine different individuals’ underlying abilities to perform this task.
Some students really stand out because they do so much better than
everyone else; if you perform better than most other students do, this
would indicate that you have more ability than they do. Remember, you
already have the ability.”

Similar to the instructions used to elicit a high level of state prove goal
orientation, instructions used to elicit a high level of state avoid goal orientation focused
the individual’s attention on the perception that their performance on the task was based
on underlying abilities. Different from the prove goal orientation manipulation, however,
the individual’s attention was also directed toward the avoidance of performing poorly

and looking incompetent:

“Successful performance on this task is based on skills and abilities that
either you do or do not already have. We are interested in comparing your
performance on this task to that of other individuals so that we can
examine different individuals’ underlying abilities to perform this task.
Some students really stand out because they do so much worse than
everyone else; if you perform worse than most other students do, this
would indicate that you have less ability than they do. Thus, you should

5]

avoid making mistakes that will make you appear incompetent.
Remember, you already have the ability.”

Subjects were randomly assigned to each of the three goal orientation
manipulation conditions. Of the 310 subjects used in the analyses, 109 were exposed to
the mastery manipulation, 102 were exposed to the prove manipulation, and 99 were
exposed to the avoid manipulation.

Positive vs. Negative Feedback Manipulation

Subjects were assigned the speciﬁc goal of performing at the 50th percentile (pilot
data were collected to establish the 50th percentile—see Appendix B). As noted
previously, subsequent to each trial subjects were given feedback indicating the
magnitude and direction of their prediction error (e.g., “You predicted that the stock price
would be $45.09. The actual stock price is $50.32. You underestimated the stock price
by $5.23”). At the end of each block, subjects were told at which percentile they
performed, followed by the statement “You met your goal” (positive feedback condition)
for individuals who score at or above the 50th percentile or “You did not meet your goal”
(negative feedback condition) for individuals who score below the 50th percentile.
Approximately 57% of subjects received positive feedback after the ﬁrst block (mastery:
58%, prove: 58%, avoid: 57%), 63% of subjects received positive feedback after the
second block (mastery: 58%, prove: 68%, avoid: 63%), and 52% of subjects received
positive feedback after the third block (mastery: 52%, prove: 53%, avoid: 51%).
Measures

State goal orientations. A 12-item survey was developed to assess individuals’
state goal orientations. Speciﬁcally, four items assessed state mastery goal orientation—

e. g., “I think that this task has the opportunity for me to develop new skills and

52

knowledge.” Four items assessed state prove goal orientation—cg, “I’m concerned
about showing that I can perform better than others on this task.” Finally, four items
assessed state avoid goal orientation—cg, “I’m concerned about letting others realize
that I have low ability on this task.” Nine of the items on this survey were adapted from
VandeWalle’s ( 1997) measure of trait goal orientations and the remaining three items
were created speciﬁcally for this study. These items were measured on a 5-point Likert-
type scale. Across the four administrations, alpha for the state mastery goal orientation
scale ranged from .67 to .82; alpha for the state prove goal orientation scale ranged from
.66 to .71; and alpha for the state avoid goal orientation scale ranged from .70 to .76.
Individual items for these three scales were averaged to create three separate scale scores
for each subject. (See Appendix C for exact wording of items.)

Trait goal orientations. A l3-item scale developed by VandeWalle (1997) was
used to assess individuals’ trait goal orientations. Speciﬁcally, ﬁve items assessed trait
mastery goal orientation—cg, “I often look for opportunities to develop new 'skills and
knowledge.” Four items assessed trait prove goal orientation—cg, “I enjoy it when
others are aware of how well I am doing.” Finally, four items assessed trait avoid goal
orientation—cg, “I’m concerned about taking on a task at school if my performance
would reveal that I had low ability.” Alphas for these three scales have been reported as
.88, .84, and .83, respectively (V andeWalle, 1997). Individuals’ trait goal orientations
were assessed twice throughout the experiment (i.e., beginning and end) so that the
relative stability of traits vs. states could be examined. Across the two administrations in
the current study, alpha for these three scales ranged from .72 to .86, .71 to .82, and .73 to

.85, respectively. These items were measured on a 5-point Likert-type scale. Individual

53

items for these three scales were averaged to create three separate scale scores for each
subject. (See Appendix D for exact wording of items.)

Self-efﬁcacy. An 8—item survey (reported alpha = .95) developed by Kozlowski
et al. (in press) was used to assess individuals’ self-efﬁcacy perceptions. An example
item is “I can meet the challenges of this task.” These items were measured on a 5-point
Likert-type scale. Across the four administrations, alpha for this scale ranged from .91 to
.96. Items for this scale were averaged to create a scale score for each subject. (See
Appendix E for exact wording of items.)

Qppnness to exgrience. A 12-item subscale (reported alpha = .76) of the NEO
Five Factor Inventory (Costa & McRae, 1992) was used to assess individuals’ openness
to experience. An example item is “Once I ﬁnd the right way to do something, I stick to
it.” These items were measured on a 5-point Likert-type scale. Similar to goal
orientations, individuals’ levels of openness to experience were assessed twice
throughout the experiment (i.e., beginning and end) so that the relative stability of traits
vs. states could be examined. Due to importance of goals and performance in the current
experiment, a differentiation between state and trait goal orientations was made whereas
openness to experience was conceptualized simply as a trait rather than as a trait and
state. Across the two administrations, alpha for this scale ranged from .71 to .72. Items
for this scale were averaged to create a scale score for each subject. (See Appendix F for
exact wording of items.)

Agreeableness. A 12—item subscale (reported alpha = .74) of the NEO Five Factor
Inventory (Costa & McRae, 1992) was used to assess individuals’ agreeableness. An

example item is “I would rather cooperate with others than compete with them.” These

54

items were measured on a 5-point Likert-type scale. Again, individuals’ levels of
agreeableness were assessed at the beginning and end of the experiment. Similar to
openness to experience, agreeableness was conceptualized only as a trait due to the nature
of the experiment. Across the two administrations, alpha for this scale ranged from .72
to .73. Items for this scale were averaged to create a scale score for each subject. (See
Appendix G for exact wording of items.)

Self-monitoring. A 25-item survey developed by Snyder (1974) was used to
assess individuals’ level of self—monitoring. An example item is “My behavior is usually
an expression of my true inner feelings, attitudes, and beliefs.” The initial survey was
measured on a true-false scale, and internal consistency reliability (KR-20) for the
original scale has been reported as .70 (Snyder, 1974). In the current study, these items
were measured on a 5-point Likert-type scale. As with the other traits in the current
study (i.e., trait goal orientations, openness to experience, and agreeableness),
individuals’ levels of self-monitoring were assessed at the beginning and end of the
experiment so that the relative stability of traits vs. states could be examined. Again,
self-monitoring was conceptualized only as a trait due to the nature of the experiment.
Across the both administrations, alpha for this scale ranged from .67 to .73. Items for this
scale were averaged to create a scale score for each subject. (See Appendix H for exact
wording of items.)

Performance evaluations. A 5-item survey was developed to assess the extent to
which individuals evaluated their performance as a success vs. failure. An example item
is “I think my performance on this task was a success.” These items were measured on a

5-point Likert—type scale. Across the three administrations, alpha for this scale ranged

55

from .88 to .94. Items for this scale were averaged to create a scale score for each
subject. (See Appendix I for exact wording of items.)

Mpg. Two 5-item surveys were developed to assess individuals’ levels of joy
and distress. The ﬁrst of these surveys assessed the extent to which individuals
experienced joy upon reception of performance feedback, ranging from elation to
depression—cg, “I am excited about my performance on this task.” The second survey
assessed the extent to which individuals experienced distress in reaction to this feedback,
ranging from relief to anxiety—cg, “Thinking about my performance on this task makes
me anxious.” These items were assessed on a 5-point Likert-type scale. Across the three
administrations, alpha for the joy scale ranged from .78 to .83, and alpha for the distress
scale ranged from .59 to .65. Individual items for these two scales were averaged to
create two separate scale scores for each subject. (See Appendix J for exact wording of
items.)

Feedback believability. At the end of the study, a 5-item survey developed
speciﬁcally for this study was administered to assess the extent to which subjects
believed the normative performance feedback that was provided subsequent to each
block. An example item is “I think that the feedback was an underestimate of my
performance.” These items were measured on a 5-point Likert—type scale. Alpha for this
scale was .77. Items for this scale were averaged to create a scale score for each subject.
(See Appendix K for exact wording of items.)

Demoggaphics. In addition to the aforementioned variables of interest,

demographic information regarding subjects’ age, sex, year in school, major, grade point

56

average, ACT or SAT scores, and plans after graduation was also collected. (See
Appendix L for exact wording of items.)
Procedure

After subjects provided their written consent to participate in the experiment (see
Appendix M), they were asked to respond to the demographics, trait goal orientations,
openness to experience, agreeableness, and self-monitoring surveys. Upon completion of
these surveys, the experimenter read some instructions to the subjects as they followed
along on their computer screens. These instructions included a general overview of the
task as well as necessary information for performing the MCPL task. Also embedded in
these instructions was one of the three goal orientation manipulations described earlier
(see Appendix N for these instructions). Subjects were then be asked to complete the
self-efﬁcacy and state goal orientations surveys, followed by the ﬁrst block of 15 trials.
Subsequent to the ﬁrst block, subjects received performance feedback (positive or
negative) and were asked to respond to the performance evaluations, affect, self-efﬁcacy,
and state goal orientations surveys. Subjects then completed the second block of 15
trials, followed by another assessment of their performance evaluations, affect, self-
efﬁcacy and state goal orientations. After completing the surveys, subjects were asked to
complete the third block of 15 trials, followed by a ﬁnal assessment of their performance
evaluations, affect, their self-efﬁcacy, and state goal orientations. Subjects were

debriefed completely (see Appendix 0) and thanked for their participation.

57

RESULTS

Means, standard deviations, scale internal consistent reliabilities, and
intercorrelations among the scales of interest are displayed in Table 1. For the remainder
of the analyses, scores on the following scales were standardized (M = 0, S_D = 1) for
each administration: trait goal orientations, state goal orientations, self-efﬁcacy,
agreeableness, openness to experiences, self-monitoring, performance evaluations, joy,
and distress.
Manipulation Checks

Positive vs. negative feedback. On average, subjects reported neutral attitudes (M
= 3.26, S_D_ = .69) toward the believability of the performance feedback given to them. In
addition, as seen in Table l, the feedback that subjects’ received was positively related to
the evaluations that they made regarding their performance (Block 1: g = .52, p < .01;
Block 2: g = .61, p < .01; Block 3: r = .54, p < .01). Finally, subjects’ responses to the
believability index did not vary reliably as a function of goal orientation manipulation
condition (E (2, 307) = 0.58, p > .05).

ﬂ] orientLion manipulation. Ideally, subjects’ levels of state mastery, prove,
and avoid goal orientations at Time 1 would have been highest when they received a
mastery, prove, or avoid goal orientation manipulation (respectively). This, however,
was not the case. Goal orientation manipulation condition did not have an effect on
subjects’ levels of state mastery (E (2, 307) = 2.51, p > .05), prove E (2, 307) = 0.32, p >
.05), or avoid goal orientation E (2, 307) = 0.14, p > .05) at Time 1 or any other time

period. These results suggest that the goal orientation manipulation failed.

58

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65

Independence of Goal Orientgions

As shown in Table l, the intercorrelations among goal orientations were
moderately high. Speciﬁcally, intercorrelations among trait goal orientations at Time 1
ranged from -.37 to .31, trait goal orientations at Time 2 ranged from -.22 to .45, state
goal orientations at Time 1 ranged from -.04 to .65, state goal orientations at Time 2
ranged from 0.00 to .65, state goal orientations at Time 3 ranged from .09 to .68, and
state goal orientations at Time 4 ranged from .10 to .72. With the exception of trait goal
orientations at Time 1, the correlation between prove and avoid goal orientations was
strongest in all of the aforementioned ranges. The lack of independence among
dependent variables poses a potential limitation to the interpretation of univariate results.
This potential limitation will be explored further in the Discussion section of this paper.
Relative Stability of Traits vs. States

As noted, individuals’ levels of trait goal orientations, openness to experience,
agreeableness, and self-monitoring were assessed twice throughout the experiment (i.e.,
beginning and end) so that the relative stability of traits vs. states could be examined.
Evidence of greater stability in trait measures (relative to state goal orientations) would
provide justiﬁcation for treating traits as traits (i.e., attributes that remain fairly stable
across time) rather than as states (i.e., attributes that ﬂuctuate across time). As listed in
Table l, test-retest reliability for the trait measures ranged from .49 to .90 (M = .75)
whereas test-retest reliability for the state measures (i.e., first vs. last administration)

ranged from .56 to .63 (M = .59). Whereas the lower-bounds of these ranges are equal

(22* = -1.27, p > .05), the upper-bounds are not equal (22* = 9.18, p_ < .01) (Steiger,

66

1980). Thus, the traits assessed in the current study remained more stable than state goal
orientations.

Iglit Goal Orientations. Situational Inﬂuences, and State Goal Orientations

 

As shown in Figure 2, Hypothesis 1 proposed that the relationship between trait
goal orientations and state goal orientations would be moderated by a goal orientation
manipulation. Speciﬁcally, it was expected that as a particular trait goal orientation
increased, the inﬂuence of an inconsistent goal orientation manipulation on the respective
state goal orientation would decrease. For example, as an individual’s trait mastery goal
orientation increased, the inﬂuence of a prove or avoid goal orientation manipulation on
subsequent state mastery goal orientation should decrease.

Three sets of hierarchical regression analyses (i.e., one set each for mastery,
prove, and avoid goal orientation) were conducted to examine the proposed relationships.
To model the main effect of the 3-level categorical variable representing manipulation
condition (i.e., mastery, prove, or avoid manipulation), 2 dummy codes were created.
These dummy codes were then multiplied by the respective trait goal orientation to
examine their interactions. The results of these hierarchical regression analyses are
described in detail below and summarized in Table 2.

Masteg goal orientation. Step 1 of the ﬁrst set of hierarchical regression analyses
consisted of state mastery goal orientation as the criterion variable and trait mastery goal
orientation as the predictor variable (32 = .1450, E (l, 308) = 52.22, p < .01). Two
dummy coded variables for the prove and avoid levels of the condition variable were
added as predictor variables in Step 2. This model revealed a signiﬁcant main effect of

trait mastery goal orientation (t (306) = 7.07, p < .01); however, the addition of the two

67

Table 2

Summary of Hiegarchical Regression Analyses for Trait GtﬂOﬁentaQns, Situational

Inﬂuences. and State Goal Orientations

 

 

Model [3 R2 AR2 F

State Mastery Goal Orientation

Step 1 .1450 52.22"
Trait mastery goal orientation .375**

Step 2 .1543 .0093 1.70
Prove goal orientation manipulation dummy code -.195
Avoid goal orientation manipulation dummy code .015

Step 3 .1595 .0052 0.93
Prove dummy code * Trait mastery goal orientation .001
Avoid dummy code * Trait mastery goal orientation .149

State Prove Goal Orientation

Step 1 .2730 115.67“
Trait prove goal orientation .523**

Step 2 .2733 .0003 0.07
Mastery goal orientation manipulation dummy code -.024
Avoid goal orientation manipulation dummy code .019

Step 3 .2747 .0004 0.08
Mastery dummy code * Trait mastery goal orientation -.012
Avoid dummy code * Trait mastery goal orientation -.046

State Avoid Goal Orientation

Step 1 .2413 97.93**
Trait avoid goal orientation .492**

Step 2 .2445 .0032 0.66
Mastery goal orientation manipulation dummy code -.127
Prove goal orientation manipulation dummy code -.118

Step 3 .2603 .0158 3.24*
Mastery dummy code * Trait mastery goal orientation -. 100
Prove dummy code * Trait mastery goal orientation -.310*

 

*p_<.05;**p_<.01

68

condition dummy codes did not reveal a signiﬁcant main effect of condition (Ali2 =
.0093, E (2, 306) = 1.70, p > .05). Finally, two product terms (i.e., the two dummy coded
condition variables multiplied by trait mastery goal orientation) were added as predictor
variables in Step 3. The addition of these interaction terms did not result in a signiﬁcant
increase in R2 (AB2 = .0052, E (2, 304) = 0.93, p_ > .05). Thus, condition did not
moderate the relationship between trait mastery goal orientation and state mastery goal
orientation.

Prove ﬂorientation. Step 1 of the second set of hierarchical regression
analyses consisted of state prove goal orientation as the criterion variable and trait prove
goal orientation as the predictor variable (32 = .2730, E (1, 308) = 115.67, p < .01). Two
dummy coded variables for the mastery and avoid levels of the condition variable were
added as predictor variables in Step 2. This model revealed a signiﬁcant effect of trait
prove goal orientation (1 (306) = 10.69, p_ < .01); however, similar to the analyses on
mastery goal orientation, the addition of the two dummy codes did not reveal a
signiﬁcant main effect of condition (A1:2 = .0003, E (2, 306) = 0.07, p > .05). Finally,
two product terms (i.e., trait prove goal orientation multiplied by the two dummy codes
from Step 2) were added as predictor variables in Step 3. Similar to the mastery goal
orientation analyses, the addition of these interaction terms did not result in a signiﬁcant
increase in R2 (AB2 = .0004, E (2, 304) = 0.08, p > .05). Thus, condition did not
moderate the relationship between trait prove goal orientation and state prove goal
orientation.

Avoid goal orientation. Step 1 of the third set of hierarchical regression analyses

conducted to examine Hypothesis 1 consisted of state avoid goal orientation as the

69

criterion variable and trait avoid goal orientation as the predictor variable (32 = .2413, E
(1, 308) = 97.93, p < .01). Two dummy coded variables for the mastery and prove levels
of the condition variable were added as predictor variables in Step 2. This model
revealed a signiﬁcant effect of trait avoid goal orientation (t (306) = 9.93, p < .01);
however, the addition of the two dummy codes did not reveal a signiﬁcant main effect of
condition (A32 = .0032, E (2, 306) = 0.66, p_ > .05). Finally, two product terms (i.e., trait
avoid goal orientation multiplied by the two dummy codes from Step 2) were added as
predictor variables in Step 3. The addition of these interaction terms resulted in a
signiﬁcant increase in R2 (A3} = .0158, E (2, 304) = 3.24, p < .05; see Figure 3).
According to Hypothesis 1, state avoid goal orientation should be highest when trait
avoid goal orientation is high and an avoid goal orientation manipulation is present. As
seen in Figure 3, this was not the case, suggesting that this signiﬁcant interaction could
be the result of Type I error. Thus, Hypothesis 1 was not supported.

Trait Goal Orientations Individual Differences and State Goal Orientations

 

Hypothesis 2 predicted that individuals’ levels of openness to experiences,
agreeableness, and self-monitoring would moderate the relationship between trait goal
orientations and state goal orientations. Speciﬁcally, this hypothesis proposed that as
these individual difference variables increased the relationship between trait goal
orientations and respective state goal orientations would decrease.

To analyze this hypothesis, three sets of hierarchical regression analyses (i.e., one
set each for openness to experiences, agreeableness, and self-monitoring) were conducted

with respect to mastery, prove, and avoid goal orientations. The results of these analyses

70

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are described in detail below and are summarized in Tables 3-5 for mastery, prove, and
avoid goal orientations, respectively.

Mastegy goal orientation. Step 1 of the ﬁrst set of hierarchical regression analyses
for mastery goal orientation consisted of state mastery goal orientation as the criterion
variable and trait mastery goal orientation and openness to experiences as predictor
variables 32 = .1547, E (2, 307) = 28.08, p < .01). The results of this step revealed a
signiﬁcant main effect of trait mastery goal orientation (1 (307) = 7.49, p < .01) but no
effect of openness to experiences (1(307) = -l.88, p > .05). Adding a product term (i.e.,
trait mastery goal orientation multiplied by openness to experiences) to the model in Step
2 did not result in a signiﬁcant increment in R2 (A32 = .0053, E (1, 306) = 1.96, p > .05).
Thus, openness to experiences did not moderate the relationship between trait mastery
goal orientation and state mastery goal orientation.

Step 1 of the second set of hierarchical regression analyses for mastery goal
orientation consisted of state mastery goal orientation as the criterion variable and trait
mastery goal orientation and agreeableness as predictor variables (32 = .1503, E (2, 307)
= 27.16, p_ < .01). The results of this step revealed a signiﬁcant main effect of trait
mastery goal orientation (1 (307) = 7.03, p < .01) but no effect of agreeableness (t (307) =
1.39, p > .05). Adding an interaction term among these two variables to the model in
Step 2 did not result in a signiﬁcant increment in R2 (A32 = .0030, E (1, 306) = 1.05, p >
.05). Thus, agreeableness did not moderate the relationship between trait mastery goal
orientation and state mastery goal orientation.

Finally, Step 1 of the third set of hierarchical regression analyses for mastery goal

orientation consisted of state mastery goal orientation as the criterion variable and trait

72

Table 3

Summm of Hierarchical Regression Analyses for Trait Ma_sterv Goal Orientation,

Individual Differences, and Stage Masten Goal Orientation

 

 

Model a R2 AR2 F

Openness to Experiences

Step 1 .1547 28.08””
Trait mastery goal orientation .401 **
Openness to experiences -. 100

Step 2 .1600 .0053 1.96
Trait mastery goal orientation * Openness to -.O66

experiences

Agreeableness

Step 1 .1503 27.16**
Trait mastery goal orientation .367**
Agreeableness .072

Step 2 .1533 .0030 1.05
Trait mastery goal orientation * Agreeableness .050

Self-Monitoring

Step 1 .1469 26.43**
Trait mastery goal orientation .373“
Self-monitoring .043

Step 2 .1469 .0000 0.00
Trait mastery goal orientation * Self-monitoring -.003

 

*p<.05; **p<.01

73

Table 4

Summary of Hierarchical Regression Aﬂlyses for Trait Prove GoeiOrientatLion, Individaal

Difference; and State Prove Goal Orientation

 

 

Model [3 R2 AR2 F

Openness to Experiences

Step 1 0.2734 57.75“
Trait prove goal orientation .523**
Openness to experiences -.019

Step 2 .2773 .0039 1.66
Trait prove goal orientation * Openness to experiences .064

Agreeableness

Step 1 .2737 57.84“
Trait prove goal orientation .518**
Agreeableness -.026

Step 2 .2805 .0068 2.92
Trait prove goal orientation * Agreeableness .087

Self-Monitoring

Step 1 .2743 58.01**
Trait prove goal orientation .512**
Self-monitoring .037

Step 2 .2764 .0021 0.88
Trait prove goal orientation * Self-monitoring .041

 

*p_<.05;**p<.01

74

Table 5

Summary of Hierarchical Regression Analyses for Trait Avoid GrﬂOrienﬁtation, Individual

Differences, and State Avoid Goal Orientation

 

 

Model [3 R2 AR2 F

Openness to Experiences

Step 1 .2425 49.14**
Trait avoid goal orientation .486“
Openness to experiences -.036

Step 2 .2460 .0035 1.42
Trait avoid goal orientation * Openness to experiences -.061

Agreeableness

Step 1 .2426 49.17**
Trait avoid goal orientation .497**
Agreeableness .037

Step 2 .2483 .0057 2.34
Trait avoid goal orientation * Agreeableness .070

Self-Monitoring

Step 1 .2413 48.82**
Trait avoid goal orientation .491
Self-monitoring .007

Step 2 .2460 .0047 1.88
Trait avoid goal orientation * Self-monitoring —.064

 

*p<.05;**p<.01

75

mastery goal orientation and self-monitoring as predictor variables (_R_2 = .1469, E (2,
307) = 26.43, p < .01). Similar to the previous analyses, the results of this step revealed a
signiﬁcant main effect of trait mastery goal orientation (1 (307) = 7.18, p < .01) but no
effect of self-monitoring (t (307) = 1.39, p > .05). Again, adding an interaction term
among these two variables to the model in Step 2 did not result in a signiﬁcant increment
in R2 (AB2 = .0000, E (1, 306) = 0.00, p > .05). Thus, similar to openness to experiences
and agreeableness, self-monitoring did not moderate the relationship between trait
mastery goal orientation and state mastery goal orientation.

Prove goal orientation. Step 1 of the ﬁrst set of hierarchical regression analyses
for prove goal orientation consisted of state prove goal orientation as the criterion
variable and trait prove goal orientation and openness to experiences as predictor
variables (I;2 = .2734, E (2, 307) = 57.75, p < .01). The results of this step revealed a
signiﬁcant main effect of trait prove goal orientation (t (307) = 10.74, p < .01) but no
effect of openness to experiences (1 (307) = -.39, p > .05). Adding an interaction term
among these two variables to the model in Step 2 did not result in a signiﬁcant increment
in R2 (AB2 = .0039, E (l, 306) = 1.66, p_ > .05). Thus, openness to experiences did not
moderate the relationship between trait prove goal orientation and state prove goal
orientation.

Step 1 of the second set of hierarchical regression analyses for prove goal
orientation consisted of state prove goal orientation as the criterion variable and trait
prove goal orientation and agreeableness as predictor variables (32 = .2737, E (2, 307) =
57.84, p_ < .01). The results of this step revealed a signiﬁcant main effect of trait prove

goal orientation (3 (307) = 10.44, p_ < .01) but no effect of agreeableness (t (307) = -.53, p

76

> .05). Adding an interaction term among these two variables to the model in Step 2 did
not result in a signiﬁcant increment in R2 (AB2 = .0068, E (1, 306) = 1.71, p > .05). Thus,
agreeableness did not moderate the relationship between trait prove goal orientation and
state prove goal orientation.

Finally, Step 1 of the third set of hierarchical regression analyses for prove goal
orientation consisted of state prove goal orientation as the criterion variable and trait
prove goal orientation and self-monitoring as predictor variables (32 = .2743, E (2, 307) =
58.01, p < .01). The results of this step revealed a signiﬁcant main effect of trait prove
goal orientation (_t_ (307) = 10.00, p < .01) but no effect of self-monitoring (t (307) = .73, p
> .05). Adding an interaction term among these two variables to the model in Step 2 did
not result in a signiﬁcant increment in R2 (A32 = .0021, E (l, 306) = 0.88, p_ > .05). Thus,
similar to openness to experiences and agreeableness, self-monitoring did not moderate
the relationship between trait and state prove goal orientation.

Avoid goal orientation. The analyses for avoid goal orientation were conducted
in an identical manner as those for mastery and prove goal orientations. Speciﬁcally,
Step 1 of the ﬁrst set of analyses consisted of state avoid goal orientation as the criterion
variable and trait avoid goal orientation and openness to experiences as predictor
variables (32 = .2425, E (2, 307) = 49.14, p < .01). The results of this step revealed a
signiﬁcant main effect of trait avoid goal orientation (1(307) = 9.65, p < .01) but no
effect of openness to experiences Q (307) = -.71, p > .05). Adding an interaction term
among these two variables to the model in Step 2 did not result in a signiﬁcant increment

in R2 (A_R_2 = .0035, E (1, 306) = 1.42, p > .05). Thus, openness to experiences did not

77

moderate the relationship between trait avoid goal orientation and state avoid goal
orientation.

Step 1 of the second set of hierarchical regression analyses for prove goal
orientation consisted of state avoid goal orientation as the criterion variable and trait
avoid goal orientation and agreeableness as predictor variables (32 = .2426, E (2, 307) =
49.17, p < .01). The results of this step revealed a signiﬁcant main effect of trait avoid
goal orientation (t (307) = 9.89, p < .01) but no effect of agreeableness (t (307) = .74, p >
.05). Adding an interaction term among these two variables to the model in Step 2 did
not result in a signiﬁcant increment in R2 (AB2 = .0057, E (1, 306) = 2.34, p > .05). Thus,
agreeableness did not moderate the relationship between trait prove goal orientation and
state prove goal orientation.

Finally, Step 1 of the third set of hierarchical regression analyses for avoid goal
orientation consisted of state avoid goal orientation as the criterion variable and trait
avoid goal orientation and self-monitoring as predictor variables (32 = .2413, E (2, 307) =
48.82, p < .01). Similar to previous analyses, the results of this step revealed a signiﬁcant
main effect of trait avoid goal orientation (t (307) = 9.82, p < .01) but no effect of self-
monitoring (t (307) = .13, p_ > .05). Again, adding an interaction term among these two
variables to the model in Step 2 did not result in a signiﬁcant increment in R2 (A1;2 =
.0047, E (1, 306) = 1.88, p > .05). Thus, none of the individual difference variables (i.e.,
openness to experiences, agreeableness, and self-monitoring) moderated the relationship
between mastery, prove, or avoid trait and state goal orientations, and Hypothesis 2 was

not supported.

78

Self-Efﬁcacy and Initial State Goal Orientatiarg

Hypothesis 3 proposed that self-efﬁcacy would have a main effect on initial (i.e.,
Time 1) state goal orientations. Speciﬁcally, it was expected that as self-efﬁcacy
increased, state mastery and prove goal orientations would increase and state avoid goal
orientation would decrease. As expected, self-efﬁcacy at Time 1 was positively related
to state mastery goal orientation (1 = .38, p < .01) and state prove goal orientation (r =
.17, p < .01) at Time 1 and negatively related to state avoid goal orientation at Time 1 (g
= -.11, p = .0561). However, these correlations were signiﬁcant only with respect to state
mastery and prove goal orientations; the relationship between self-efﬁcacy and state
avoid goal orientation was marginal. Thus, Hypothesis 3 received partial support.
Self-Efﬁcacy and Subsequent State Goal Orierﬂrtions

Similar to Hypothesis 3, Hypothesis 10 proposed that self-efﬁcacy would have a
main effect on state goal orientations. Different from Hypothesis 3, however, the effects
proposed by Hypothesis 10 stem from the dynamic nature of the operational model (see
Figure 2). To test these effects, state goal orientations from the previous iteration of the
cycle were partialled; thus, results reported with respect to Hypothesis 10 are based on
Type I SS.

Masteﬂ goal orientation. State mastery goal orientation at Time 2 was entered
into a regression analysis as the criterion, and state mastery goal orientation at Time 1
and self-efﬁcacy at Time 2 were entered as predictors. Results revealed that after
partialling the effects of state mastery goal orientation at Time 1 (1 (307 ) = 14.10, p <
.01), self-efﬁcacy at Time 2 had a signiﬁcant main effect on state mastery goal

orientation at Time 2 (t (307) = 5.25, p < .01).

79

Next, state mastery goal orientation at Time 3 was entered into a regression
analysis as the criterion, and state mastery goal orientation at Time 2 and self-efﬁcacy at
Time 3 were entered as predictors. Results revealed that after partialling the effects of
state mastery goal orientation at Time 2 (t (307) = 19.41, p < .01), self-efﬁcacy at Time 3
had a signiﬁcant main effect on state mastery goal orientation at Time 3 (t (307) = 5.14, p
< .01).

Finally, state mastery goal orientation at Time 4 was entered into a regression
analysis as the criterion, and state mastery goal orientation at Time 3 and self-efﬁcacy at
Time 4 were entered as predictors. Results revealed that after partialling the effects of
state mastery goal orientation at Time 3 (t (307) = 24.88, p_ < .01), self-efﬁcacy at Time 4
had a signiﬁcant main effect on state mastery goal orientation at Time 4 Q (307) = 4.61 , p
< .01). Thus, with regard to state mastery goal orientation, the proposed effect of self—
efﬁcacy was observed at Times 2-4.

Prove goal orientation. State prove goal orientation at Time 2 was entered into a
regression analysis as the criterion, and state prove goal orientation at Time 1 and self-
efﬁcacy at Time 2 were entered as predictors. Again, results revealed that after
partialling the effects of state prove goal orientation at Time 1 (t (307) = 17.61, p < .01),
self-efﬁcacy at Time 2 had a signiﬁcant main effect on state prove goal orientation at
Time 2 (t (307) = 3.54, p < .01).

Next, state prove goal orientation at Time 3 was entered into a regression analysis
as the criterion, and state prove goal orientation at Time 2 and self-efﬁcacy at Time 3
were entered as predictors. Again, results revealed that after partialling the effects of

state prove goal orientation at Time 2 (t (307) = 22.57, p < .01), self-efﬁcacy at Time 3

80

had a signiﬁcant main effect on state prove goal orientation at Time 3 (t (307) = 3.12, p <
.01).

Finally, state prove goal orientation at Time 4 was entered into a regression
analysis as the criterion, and state prove goal orientation at Time 3 and self-efﬁcacy at
Time 4 were entered as predictors. Contrary to expectations, results revealed that after
partialling the effects of state prove goal orientation at Time 3 (t (307) = 23.88, p < .01),
self-efﬁcacy at Time 4 did not have a signiﬁcant main effect on state prove goal
orientation at Time 4 (t (307) = .28, p > .05). Thus, with regard to state prove goal
orientation, the proposed effect of self-efﬁcacy was observed at Times 2 and 3 but not
Time 4.

Avoid ggl orientation. State avoid goal orientation at Time 2 was entered into a
regression analysis as the criterion, and state avoid goal orientation at Time 1 and self-
efﬁcacy at Time 2 were entered as predictors. Results revealed that after partialling the
effects of state avoid goal orientation at Time 1 (t (307) = 20.50, p_ < .01), self-efﬁcacy at
Time 2 did not have a signiﬁcant main effect on state avoid goal orientation at Time 2 (t
(307) = .81, p > .05).

Next, state avoid goal orientation at Time 3 was entered into a regression analysis
as the criterion, and state avoid goal orientation at Time 2 and self-efﬁcacy at Time 3
were entered as predictors. Results revealed that after partialling the effects of state
avoid goal orientation at Time 2 (t (307) = 25.02, p_ < .01), self-efﬁcacy at Time 3 did not
have a signiﬁcant main effect on state avoid goal orientation at Time 3 Q (307) = .31, p_ >

.05).

81

Finally, state avoid goal orientation at Time 4 was entered into a regression
analysis as the criterion, and state avoid goal orientation at Time 3 and self-efﬁcacy at
Time 4 were entered as predictors. Results revealed that after partialling the effects of
state avoid goal orientation at Time 3 (t (307) = 28.88, p < .01), self-efﬁcacy at Time 4
did not have a signiﬁcant main effect on state avoid goal orientation at Time 4 (t (307) =
.15, p > .05). Thus, with regard to state avoid goal orientation, the proposed effects of
self—efﬁcacy were not observed at Times 2—4. Further, similar to Hypothesis 3,
Hypothesis 10 received partial support.

S_tate Goal Orientations, Feedback Sign, and Performance Evaluations

Hypothesis 4 predicted that state goal orientations and feedback sign would
interact in their effects on individuals’ performance evaluations. To analyze this
hypothesis, three sets of hierarchical regression analyses (i.e., one set each for Time 1,
Time 2, and Time 3) were conducted with respect to state mastery, prove, and avoid goal
orientations. Further, for Times 2 and 3, performance evaluations from Times 1 and 2
(respectively) were partialled before proceeding with the analyses. The results of these
analyses are described in detail below and are summarized in Tables 6-8 for mastery,
prove, and avoid goal orientations, respectively.

Masteg goal orientation. With respect to mastery goal orientation, Hypothesis 4
predicted that as individuals’ levels of state mastery goal orientation increased, positive
feedback would lead to performance evaluations of success and negative feedback would
lead to performance evaluations of neither success nor failure. Step 1 of the fast set of
hierarchical regression analyses for mastery goal orientation consisted of subjects’

performance evaluations at Time 1 as the criterion, and state mastery goal orientation at

82

Table 6

Summm of Hierarchical Regression Analyses for State way Go

Siga, and Performalce Evaluations

a1 Orientation Feedback

 

 

 

Model [3 R2 AR2 F

Time 1

Step 1 .2801 59.72**
State mastery goal orientation -.109*
Feedback sign 1.041“

Step 2 .2807 .0006 0.27
State mastery goal orientation * Feedback sign —.051

Time 2

Step 1 .1624 59.74“
Performance evaluations (Time 1) .404"

Step 2 .4527 .2903 81.15“
State mastery goal orientation -.051
Feedback sign 1.127"

Step 3 .4528 .0001 0.04
State mastery goal orientation * Feedback sign -.017

Time 3

Step 1 .2696 113.70**
Performance evaluations (Time 2) .519**

Step 2 .4615 .1919 54.52“
State mastery goal orientation .031
Feedback sign .898“

Step 3 .4616 .0001 0.06
State mastery goal orientation * Feedback sign .022

 

*p<.05; **p<.01

83

Table 7

Summary of Hierarchical Regression Analyses for State Prove Goal Orie

 

Sig, and Performﬂe Evaluations

ntation Feedback

 

 

 

Model [3 R2 AR2 F

Time 1

Step 1 .2716 5723’”
State prove goal orientation -.056
Feedback sign 1062'”

Step 2 .2719 .0003 0.14
State prove goal orientation * Feedback sign .034

Time 2

Step 1 .1624 59.74"
Performance evaluations (Time 2) .404**

Step 2 .4504 .2880 80.17“
State prove goal orientation -.014
Feedback sign 1.139M

Step 3 .4533 .0029 1.61
State prove goal orientation * Feedback sign .111

Time 3

Step 1 .2696 113.70“
Performance evaluations (Time 2) .519**

Step 2 .4609 .1913 54.29**
State prove goal orientation -.014
Feedback sign .897"

Step 3 .4670 .0061 3.50
State prove goal orientation * Feedback sign .166

 

*p<.05; **p<.01

84

Table 8

Summgy of Hierarchical Regrassion Analyses for State Avoid Goal Orimion. Feedba_c_k_

Sigr_l, and Performance Evaluations

 

 

Model B R2 AR2 F

Time 1

Step 1 .2685 56.25“
State avoid goal orientation .001
Feedback sign 1.047**

Step 2 .2725 .0040 1.66
State avoid goal orientation * Feedback sign .130

Time 2

Step 1 .1624 59.74**
Performance evaluations (Time 1) .404**

Step 2 .4505 .2881 80.22**
State avoid goal orientation -.018
Feedback sign 1.139**

Step 3 .4518 .0013 0.71
State avoid goal orientation * Feedback sign .074

Time 3

Step 1 .2696 113.70**
Performance evaluations (Time 2) .519**

Step 2 .4634 .1938 55.26“
State avoid goal orientation -.055
Feedback sign .892**

Step 3 .4649 .0015 0.85
State avoid goal orientation * Feedback sign .080

 

*p<.05; **p<.01

85

Time 1 and feedback sign at Time 1 as predictors (132 = .2801, E (2, 307) = 59.72, p <
.01). The results of this step revealed signiﬁcant main effects of state mastery goal
orientation Q (307) = -2.22, p < .05) and feedback sign Q (307) = 10.63, p < .01). Adding
an interaction term among these two variables to the model in Step 2 did not result in a
signiﬁcant increment in R2 (A32 = .0006, E (1, 306) = 0.27, p > .05).

Step 1 of the second set of hierarchical regression analyses for mastery goal
orientation consisted of subjects’ performance evaluations at Time 2 as the criterion and
their performance evaluations at Time 1 as the predictor (32 = .1624, E (1, 308) = 59.74,
p_ < .01). Next, state mastery goal orientation at Time 2 and feedback sign at Time 2 were
added to the model as predictors in Step 2. The results of this second step revealed a
signiﬁcant main effect of feedback sign Q (306) = 12.54, p < .01) but no effect of state
mastery goal orientation Q (306) = -1.19, p_ > .05). Further, the addition of an interaction
term among state mastery goal orientation and feedback sign to the model in Step 3 did
not result in a signiﬁcant increment in R2 (A_13_2 = .0001, E (l, 305) = 0.04, p > .05).

Finally, Step 1 of the third set of hierarchical regression analyses for mastery goal
orientation consisted of subjects’ performance evaluations at Time 3 as the criterion and
their performance evaluations at Time 2 as the predictor (32 = .2696, E (1 , 308) = 113.70,
p < .01). Next, state mastery goal orientation at Time 3 and feedback sign at Time 3 were
added to the model as predictors in Step 2. The results of this second step revealed a
signiﬁcant main effect of feedback sign Q (306) = 10.44, p < .01) but no effect of state
mastery goal orientation Q (306) = 0.03, p > .05). Again, the addition of an interaction
term among state mastery goal orientation and feedback sign to the model in Step 3 did

not result in a signiﬁcant increment in R2 (A1;2 = .0001, E (1, 305) = 0.06, p > .05). Thus,

86

the hypothesized interaction between state mastery goal orientation and feedback sign
was not observed at Times 1, 2, or 3.

Prove goal orientation. With respect to prove goal orientation, Hypothesis 4
predicted that as individuals’ levels of state prove goal orientation increased, positive
feedback would lead to performance evaluations of success and negative feedback would
lead to performance evaluations of failure. Step 1 of the ﬁrst set of hierarchical
regression analyses for prove goal orientation consisted of subjects’ performance
evaluations at Time 1 as the criterion, and state prove goal orientation at Time 1 and
feedback sign at Time 1 as predictors (32 = .2716, E (2, 307) = 57.23, p < .01). The
results of this step revealed a signiﬁcant main effect of feedback sign Q (307) = 10.70, p
< .01) but no effect of state prove goal orientation Q (307) = -l.13, p > .05). Adding an
interaction term among these two variables to the model in Step 2 did not result in a
signiﬁcant increment in R2 (AB2 = .0003, E (l, 306) = 0.14, p > .05).

Step 1 of the second set of hierarchical regression analyses for prove goal
orientation consisted of subjects’ performance evaluations at Time 2 as the criterion and
their performance evaluations at Time 1 as the predictor (32 = .1624, E (1, 308) = 59.74,
p < .01). Next, state prove goal orientation at Time 2 and feedback sign at Time 2 were
added to the model as predictors in Step 2. The results of this second step revealed a
signiﬁcant main effect of feedback sign Q (306) = 12.61, p < .01) but no effect of state
prove goal orientation Q (306) = -0.33, p > .05). Further, the addition of an interaction
term among state prove goal orientation and feedback sign to the model in Step 3 did not

result in a signiﬁcant increment in R2 (A32 = .0029, E (1, 305) = 1.61, p > .05).

87

Finally, Step 1 of the third set of hierarchical regression analyses for prove goal
orientation consisted of subjects’ performance evaluations at Time 3 as the criterion and
their performance evaluations at Time 2 as the predictor (32 = .2696, E (1, 308) = 113.70,
p < .01). Next, state prove goal orientation at Time 3 and feedback sign at Time 3 were
added to the model as predictors in Step 2. The results of this second step revealed a
signiﬁcant main effect of feedback sign Q (306) = 10.42, p < .01) but no effect of state
prove goal orientation Q (306) = -0.30, p_ > .05). The addition of an interaction term
among state prove goal orientation and feedback sign to the model in Step 3 resulted in a
marginally signiﬁcant increment in 180332 = .0061, E (1, 305) = 3.50, p = .0620). As
seen in Figure 4, with an increase in state prove goal orientation positive feedback led to
performance evaluations of success and negative feedback led to performance evaluations
of failure, as hypothesized. Thus, the hypothesized interaction between state prove goal
orientation and feedback sign was not observed at Times 1 or 2, but it was marginally
signiﬁcant and in the correct direction at Time 3.

Avoid goal orientation. Similar to prove goal orientation, Hypothesis 4 predicted
that as individuals’ levels of state avoid goal orientation increased, positive feedback
would lead to performance evaluations of success and negative feedback would lead to
performance evaluations of failure. Step 1 of the ﬁrst set of hierarchical regression
analyses for avoid goal orientation consisted of subjects’ performance evaluations at
Time 1 as the criterion, and state avoid goal orientation at Time 1 and feedback sign at
Time 1 as predictors (32 = .2685, E (2, 307) = 56.35, p < .01). The results of this step
revealed a signiﬁcant main effect of feedback sign Q (307) = 10.53, p < .01) but no effect

of state avoid goal orientation Q (307) = 0.02, p > .05). Adding an interaction term

88

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among these two variables to the model in Step 2 did not result in a signiﬁcant increment
in R2 (1332 = .0004, 5(1, 306) = 1.66, p > .05).

Step 1 of the second set of hierarchical regression analyses for avoid goal
orientation consisted of subjects’ performance evaluations at Time 2 as the criterion and
their performance evaluations at Time 1 as the predictor (112 = .1624, E (l, 308) = 59.74,
p < .01). Next, state avoid goal orientation at Time 2 and feedback sign at Time 2 were
added to the model as predictors in Step 2. The results of this second step revealed a
signiﬁcant main effect of feedback sign Q (306) = 12.65, p_ < .01) but no effect of state
avoid goal orientation Q (306) = -0.43, p > .05). Further, the addition of an interaction
term among state avoid goal orientation and feedback sign to the model in Step 3 did not
result in a signiﬁcant increment in R2 (A32 = .0013, E (1, 305) = 0.71, p > .05).

Finally, Step 1 of the third set of hierarchical regression analyses for avoid goal
orientation consisted of subjects’ performance evaluations at Time 3 as the criterion and
their performance evaluations at Time 2 as the predictor (32 = .2696, E (l, 308) = 113.70,
p_ < .01). Next, state avoid goal orientation at Time 3 and feedback sign at Time 3 were
added to the model as predictors in Step 2. The results of this second step revealed a
signiﬁcant main effect of feedback sign Q (306) = 10.38, p < .01) but no effect of state
avoid goal orientation Q (306) = -l .25, p > .05). Again, the addition of an interaction
term among state avoid goal orientation and feedback sign to the model in Step 3 did not
result in a signiﬁcant increment in R2 (A32 = .0015, E (1, 305) = 0.85, p_ > .05). Thus, the
hypothesized interaction between state avoid goal orientation and feedback sign was not
observed at Times 1, 2, or 3. Further, collapsing across all three goal orientations,

Hypothesis 4 received only partial support (i.e., the marginal interaction between state

90

prove goal orientation and feedback sign in their effects on performance evaluations at
Time 3).
T_rait Goal Orientations, Feedback Siga, and Performance Evaluations

Similar to Hypothesis 4 with respect to state goal orientations, Hypothesis 5
predicted that trait goal orientations and feedback sign would interact in their effects on
individuals’ performance evaluations. To analyze this hypothesis, three sets of
hierarchical regression analyses (i.e., one set each for Time 1, Time 2, and Time 3) were
conducted with respect to trait mastery, prove, and avoid goal orientations. Further, for
Times 2 and 3, performance evaluations from Times 1 and 2 (respectively) were
partialled before proceeding with the analyses. The results of these analyses are
described in detail below and are summarized in Tables 9-11 for mastery, prove, and
avoid goal orientations, respectively.

Masteg goal orientation. With respect to mastery goal orientation, Hypothesis 5
predicted that as individuals’ levels of trait mastery goal orientation increased, positive
feedback would lead to performance evaluations of success and negative feedback would
lead to performance evaluations of neither success nor failure. Step 1 of the ﬁrst set of
hierarchical regression analyses for mastery goal orientation consisted of subjects’
performance evaluations at Time 1 as the criterion, and trait mastery goal orientation at
Time 1 and feedback sign at Time 1 as predictors $2 = .2693, E (2, 307) = 56.58, p <
.01). The results of this step revealed a signiﬁcant main effect of feedback sign Q (307) =
10.64, p < .01) but no effect of trait mastery goal orientation Q (307) = -0.57, p > .05).
Adding an interaction term among these two variables to the model in Step 2 did not

result in a signiﬁcant increment in R2 (A32 = .0007, E (1, 306) = 0.30, 2 > .05).

91

Table 9

Summary of Hierarchical Regrassion Analﬁes for Trait Mastegy Goal Orientation, Feedback

Siga, and Performance Evaluatiga

 

 

Model B R2 AR2 F

Time 1

Step 1 .2693 56.58“
Trait mastery goal orientation -.028
Feedback sign 1.050"

Step 2 .2700 .0007 0.30
Trait mastery goal orientation * Feedback sign -.055

Time 2

Step 1 .1624 59.74**
Performance evaluations (Time 1) .404**

Step 2 .4510 .2886 80.43"
Trait mastery goal orientation -.029
Feedback sign 1,134“

Step 3 .4511 .0001 0.05
Trait mastery goal orientation * Feedback sign .020

Time 3

Step 1 .2696 113.70**
Performance evaluations (Time 2) .519**

Step 2 .4623 .1927 54.83**
Trait mastery goal orientation -.041
Feedback sign .897“

Step 3 .4624 .0001 0.04
Trait mastery goal orientation * Feedback sign -.018

 

*p<.05; **p<.01

92

Table 10

Summary of Hiegrchicg Regression Analyses for Trait Prove Goal Orien

Sng, and Performnce Evaluations

tation Feedback

 

 

 

Model B R2 AR’ F

Time 1

Step 1 .2727 57.54M
Trait prove goal orientation -.064
Feedback sign 1049’”

Step 2 .2799 .0072 3.06
Trait prove goal orientation * Feedback sign .171

Time 2

Step 1 .1624 59.74**
Performance evaluations (Time 2) .404**

Step 2 .4502 .2878 80.09"
Trait prove goal orientation .001
Feedback sign 1.136M

Step 3 .4599 .0097 5.48*
Trait prove goal orientation * Feedback sign 208*

Time 3

Step 1 .2696 “3.70“
Performance evaluations (Time 2) .519**

Step 2 .4643 .1947 55.61“
Trait prove goal orientation -.060
Feedback sign .891 **

Step 3 .4837 .0194 11.49“
Trait prove goal orientation * Feedback sign .282“

 

*p<.05;**p<.01

93

Table 11

Summag of Hierarchical Regrassion Analyses for TLait Avoid Gog Orientation. Feedlggl;

Siga, and Performaace Evaﬂrtions

 

 

Model [3 R2 AR2 F

Time 1

Step 1 .2704 56.90**
Trait avoid goal orientation -.043
Feedback sign 1.05**

Step 2 .2831 .0127 5.43*
Trait avoid goal orientation * Feedback sign .228*

Time 2

Step 1 .1624 59.74**
Performance evaluations (Time 1) .404”

Step 2 .4504 .2880 80.17“
Trait avoid goal orientation .015
Feedback sign 1.135**

Step 3 .4601 .0097 5.48*
Trait avoid goal orientation * Feedback sign .207*

Time 3

Step 1 .2696 113.70MI
Performance evaluations (Time 2) .519**

Step 2 .4608 .1912 54.25“
Trait avoid goal orientation -.009
Feedback sign .896"

Step 3 .4673 .0065 3.72
Trait avoid goal orientation * Feedback sign .163

 

*p<.05;**p<.01

94

Step 1 of the second set of hierarchical regression analyses for mastery goal
orientation consisted of subjects’ performance evaluations at Time 2 as the criterion and
their performance evaluations at Time 1 as the predictor (132 = .1624, E (1, 308) = 59.74,
p < .01). Next, trait mastery goal orientation at Time 2 and feedback sign at Time 2 were
added to the model as predictors in Step 2. The results of this second step revealed a
signiﬁcant main effect of feedback sign Q (306) = 12.63, p < .01) but no effect of trait
mastery goal orientation Q (306) = -0.68, p > .05). Further, the addition of an interaction
term among trait mastery goal orientation and feedback sign to the model in Step 3 did
not result in a signiﬁcant increment in R2 (A32 = .0001, E (1, 305) = 0.05, p > .05).

Finally, Step 1 of the third set of hierarchical regression analyses for mastery goal
orientation consisted of subjects’ performance evaluations at Time 3 as the criterion and
their performance evaluations at Time 2 as the predictor (32 = .2696, E (l, 308) = 113.70,
p_ < .01). Next, trait mastery goal orientation at Time 3 and feedback sign at Time 3 were
added to the model as predictors in Step 2. The results of this second step revealed a
signiﬁcant main effect of feedback sign Q (306) = 10.43, p_ < .01) but no effect of trait
mastery goal orientation Q (306) = -0.97, p > .05). Again, the addition of an interaction
term among trait mastery goal orientation and feedback sign to the model in Step 3 did
not result in a signiﬁcant increment in R2 (A32 = .0001, E (1, 305) = 0.04, p > .05). Thus,
the hypothesized interaction between trait mastery goal orientation and feedback sign was
not observed at Times 1, 2, or 3.

Prove goal orientation. With respect to prove goal orientation, Hypothesis 5
predicted that as individuals’ levels of trait prove goal orientation increased, positive

feedback would lead to performance evaluations of success and negative feedback would

95

lead to performance evaluations of failure. Step 1 of the ﬁrst set of hierarchical
regression analyses for prove goal orientation consisted of subjects’ performance
evaluations at Time 1 as the criterion, and trait prove goal orientation at Time 1 and
feedback sign at Time 1 as predictors (32 = .2727, E (2, 307) = 57.54, p_ < .01). The
results of this step revealed a signiﬁcant main effect of feedback sign Q (307) = 10.66, p_
< .01) but no effect of trait prove goal orientation Q (307) = -1.32, p_ > .05). Adding an
interaction term among these two variables to the model in Step 2 did not result in a
signiﬁcant increment in R2 (A32 = .0072, E (1, 306) = 3.06, p > .05).

Step 1 of the second set of hierarchical regression analyses for prove goal
orientation consisted of subjects’ performance evaluations at Time 2 as the criterion and
their performance evaluations at Time 1 as the predictor $2 = .1624, E (1, 308) = 59.74,
p < .01). Next, trait prove goal orientation at Time 2 and feedback sign at Time 2 were
added to the model as predictors in Step 2. The results of this second step revealed a
signiﬁcant main effect of feedback sign Q (306) = 12.65, p_ < .01) but no effect of trait
prove goal orientation Q (306) = 0.03, p > .05). The addition of an interaction term
among trait prove goal orientation and feedback sign to the model in Step 3 resulted in a
signiﬁcant increment in R2 (A32 = .0097, E (1, 305) = 5.48, a < .05). As predicted by
Hypothesis 5, with an increase in trait prove goal orientation positive feedback led to
performance evaluations of success and negative feedback led to performance evaluations
of failure (see Figure 5).

Finally, Step 1 of the third set of hierarchical regression analyses for prove goal
orientation consisted of subjects’ performance evaluations at Time 3 as the criterion and

their performance evaluations at Time 2 as the predictor 012 = .2696, E (1, 308) = 113.70,

96

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p_ < .01). Next, trait prove goal orientation at Time 3 and feedback sign at Time 3 were
added to the model as predictors in Step 2. The results of this second step revealed a
signiﬁcant main effect of feedback sign Q (306) = 10.38, p < .01) but no effect of trait
prove goal orientation Q (306) = -1.43, p_ > .05). Again, the addition of an interaction
term among trait prove goal orientation and feedback sign to the model in Step 3 resulted
in a signiﬁcant increment in R2 (AB2 = .0230, E (1, 305) = 11.49, p < .01). As seen in
Figure 6, performance evaluations in response to positive feedback remained relatively
stable regardless of trait prove goal orientation. However, as predicted, increases in trait
prove goal orientation led to performance evaluations of failure when subjects received
negative feedback. Thus, the hypothesized interaction between trait prove goal
orientation and feedback sign was observed at Times 2 and 3 (partially), but not at Time
1.

Avoid goal orientation. Similar to prove goal orientation, Hypothesis 5 predicted
that as individuals’ levels of trait avoid goal orientation increased, positive feedback
would lead to performance evaluations of success and negative feedback would lead to
performance evaluations of failure. Step 1 of the ﬁrst set of hierarchical regression
analyses for avoid goal orientation consisted of subjects’ performance evaluations at
Time 1 as the criterion, and trait avoid goal orientation at Time 1 and feedback sign at
Time 1 as predictors (32 = .2704, E (2, 307) = 56.90, p < .01). The results of this step
revealed a signiﬁcant main effect of feedback sign Q (307) = 10.64, p < .01) but no effect
of trait avoid goal orientation Q (307) = -0.89, p_ > .05). Adding an interaction term
among these two variables to the model in Step 2 resulted in a signiﬁcant increment in R2

(AR__2 = .0127, E (1, 306) = 5.43, p_ < .05). As predicted, as trait avoid goal orientation

98

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increased positive feedback led to performance evaluations of success and negative
feedback led to performance evaluations of failure (see Figure 7). Step 1 of the second set
of hierarchical regression analyses for avoid goal orientation consisted of subjects’
performance evaluations at Time 2 as the criterion and their performance evaluations at
Time 1 as the predictor $2 = .1624, E (1, 308) = 59.74, p_ < .01). Next, trait avoid goal
orientation at Time 2 and feedback sign at Time 2 were added to the model as predictors
in Step 2. The results of this second step revealed a signiﬁcant main effect of feedback
sign Q (306) = 12.63, p < .01) but no effect of trait avoid goal orientation Q (306) = 0.36,
p > .05). Further, the addition of an interaction term among trait avoid goal orientation
and feedback sign to the model in Step 3 resulted in a signiﬁcant increment in R2 (AB2 =
.0097, E (1, 305) = 5.48, p < .05). As hypothesized, as trait avoid goal orientation
increased positive feedback led to performance evaluations of success and negative
feedback led to performance evaluations of failure (see Figure 8).

Finally, Step 1 of the third set of hierarchical regression analyses for avoid goal
orientation consisted of subjects’ performance evaluations at Time 3 as the criterion and
their performance evaluations at Time 2 as the predictor (32 = .2696, E (1, 308) = 113.70,
p < .01). Next, trait avoid goal orientation at Time 3 and feedback sign at Time 3 were
added to the model as predictors in Step 2. The results of this second step revealed a
signiﬁcant main effect of feedback sign Q (306) = 10.41, p < .01) but no effect of trait
avoid goal orientation Q (306) = -0.22, p_ > .05). Further, the addition of an interaction
term among trait avoid goal orientation and feedback sign to the model in Step 3 resulted

in a marginally signiﬁcant increment in R2 (A_R_2 = .0065, 1:10. 305) = 3.72, p = .0540).

100

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As predicted by Hypothesis 5, as trait avoid goal orientation increased positive feedback
led to performance evaluations of success and negative feedback led to performance
evaluations of failure (see Figure 9). Thus, the hypothesized interaction between trait
avoid goal orientation and feedback sign was observed at Times 1, 2, and 3 (marginal).
Further, across all three goal orientations, Hypothesis 5 received partial support.

State Goal Orientations. Feedback Sign, and Affective Reactions

Hypothesis 6 predicted that state goal orientations and feedback sign would
interact in their effects on individuals’ experience of affect. To analyze this hypothesis,
three sets of hierarchical regression analyses (i.e., one set each for Time 1, Time 2, and
Time 3) were conducted with respect to state mastery, prove, and avoid goal orientations.
Further, for Times 2 and 3, affective reactions from Times 1 and 2 (respectively) were
partialled before proceeding with the analyses. The results of these analyses are
described in detail below and are summarized in Tables 12-14 for mastery, prove, and
avoid goal orientations, respectively.

Mastery goal orientation. With respect to mastery goal orientation, Hypothesis 6
predicted that as individuals’ levels of state mastery goal orientation increased, positive
feedback would lead to positive levels of joy (i.e., elation) and negative feedback would
lead to negative levels of joy (i.e., depression). Step 1 of the ﬁrst set of hierarchical
regression analyses for mastery goal orientation consisted of subjects’ levels of joy at
Time 1 as the criterion, and state mastery goal orientation at Time 1 and feedback sign at
Time 1 as predictors L82 = .2372, E (2, 307) = 47.73, p < .01). The results of this step
revealed a signiﬁcant main effect of feedback sign (1 (307) = 9.53, p < .01) but no effect

of state mastery goal orientation (t (307) = -l.85, p_ > .05). Adding an interaction term

103

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104

Table 12

Summa_ry of Hierarchical Regression Analyses for State Ma_stery G

Sign, and the Experience of m

oal Orientation Feedback

 

 

 

Model 13 R2 AR2 F

Time 1

Step 1 .2372 47.73**
State mastery goal orientation -.094
Feedback sign .963**

Step 2 .2372 .0000 0.00
State mastery goal orientation * Feedback sign -.003

Time 2

Step 1 .2160 84.88“
Joy (Time 1) .464**

Step 2 .3858 .1698 42.30“
State mastery goal orientation .014
Feedback sign .865**

Step 3 .3863 .0005 0.21
State mastery goal orientation * Feedback sign .044

Time 3

Step 1 .2980 130.78**
Joy (Time 2) .544“

Step 2 .4095 .1115 28.89"
State mastery goal orientation .082
Feedback sign .660“

Step 3 .4125 .0030 1.54
State mastery goal orientation * Feedback sign .115

 

*p_< .05; **p<.01

105

Table 13

Summary of Hierarchical Regr_ession Analyses for State Prove Goal Orientation, Feedback

Sign, and the Experience of Joy

 

 

Model B R2 AR2 F

Time 1

Step 1 .2549 52.51“
State prove goal orientation -. 164**
Feedback sign 1012’”

Step 2 .2558 .0009 0.40
State prove goal orientation * Feedback sign .063

Time 2

Step 1 .2160 84.88“
Joy (Time 1) .464**

Step 2 .3878 .1718 42.94“
State prove goal orientation .047
Feedback sign .851"

Step 3 .3901 .0023 1.14
State prove goal orientation * Feedback sign .099

Time 3

Step 1 .2980 130.78“
Joy (Time 2) .544"

Step 2 .4038 .1058 27.15**
State prove goal orientation -.013
Feedback sign .655**

Step 3 .4198 .0160 8.41**
State prove goal orientation * Feedback sign .266"

 

*p<.05;**p_<.01

106

Table 14

Summa_ry of Hierarchical Reggession Analyses for State Avoid Goal Orientation, Feedback

Sign, and the Experience of Distress

 

 

Model B R2 AR2 F

Time 1

Step 1 .1860 3507'"
State avoid goal orientation -.063
Feedback sign .878**

Step 2 .2247 .0387 15.29**
State avoid goal orientation * Feedback sign .406**

Time 2

Step 1 .2806 120.15“
Distress (Time 1) .531**

Step 2 .3680 .0874 21.16**
State avoid goal orientation -.082
Feedback sign .617**

Step 3 .3692 .0012 0.59
State avoid goal orientation * Feedback sign -.072

Time 3

Step 1 .3943 200.47**
Distress (Time 2) .626**

Step 2 .4337 .0394 1064’”
State avoid goal orientation -.064
Feedback sign .383**

Step 3 .4530 .0193 1076“
State avoid goal orientation * Feedback sign 2.90“

 

*n<.05;**p_<.01

107

among these two variables to the model in Step 2 did not result in a signiﬁcant increment
in R2 (An2 = 0000,}: (1, 306) = 0.00, p > .05).

Step 1 of the second set of hierarchical regression analyses for mastery goal
orientation consisted of subjects’ levels of joy at Time 2 as the criterion and their levels
of joy at Time 1 as the predictor (32 = .2160, E (1, 308) = 84.88, p < .01). Next, state
mastery goal orientation at Time 2 and feedback sign at Time 2 were added to the model
as predictors in Step 2. The results of this second step revealed a signiﬁcant main effect
of feedback sign (t (306) = 9.19, p_ < .01) but no effect of state mastery goal orientation (1
(306) = 0.31, n > .05). Further, the addition of an interaction term among state mastery
goal orientation and feedback sign to the model in Step 3 did not result in a signiﬁcant
increment in R2 (A32 = .0005, E (1, 305) = 0.21, p_ > .05).

Finally, Step 1 of the third set of hierarchical regression analyses for mastery goal
orientation consisted of subjects’ levels of joy at Time 3 as the criterion and their levels
of joy at Time 2 as the predictor (32 = .2980, E (1, 308) = 130.78, p < .01). Next, state
mastery goal orientation at Time 3 and feedback sign at Time 3 were added to the model
as predictors in Step 2. The results of this second step revealed a signiﬁcant main effect
of feedback sign (1 (306) = 7.46, n < .01) but no effect of state mastery goal orientation (1
(306) = 1.75, p > .05). Again, the addition of an interaction term among state mastery
goal orientation and feedback sign to the model in Step 3 did not result in a signiﬁcant
increment in R2 (A32 = .0030, E (1, 305) = 1.54, p_ > .05). Thus, the hypothesized
interaction between state mastery goal orientation and feedback sign was not observed at

Times 1, 2, or 3.

108

Prove gonl orientaLtion. Similar to state mastery goal orientation, Hypothesis 6
predicted that as individuals’ levels of state prove goal orientation increased, positive
feedback would lead to positive levels of joy (i.e., elation) and negative feedback would
lead to negative levels of joy (i.e., depression). Step 1 of the ﬁrst set of hierarchical
regression analyses for prove goal orientation consisted of subjects’ levels of joy at Time
1 as the criterion, and state prove goal orientation at Time 1 and feedback sign at Time 1
as predictors (32 = .2549, E (2, 307) = 52.51, p_ < .01). The results of this step revealed
signiﬁcant main effects of state prove goal orientation (1 (307) = -3.29, n < .01) and
feedback sign (1 (307) = 10.05, p_ < .01). Adding an interaction term among these two
variables to the model in Step 2 did not result in a signiﬁcant increment in R2 (A_R_2 =
.0009, E (1, 306) = 0.40, n > .05).

Step 1 of the second set of hierarchical regression analyses for prove goal
orientation consisted of subjects’ levels of joy at Time 2 as the criterion and their levels
of joy at Time 1 as the predictor (132 = .2160, E (1, 308) = 84.88, E < .01). Next, state
prove goal orientation at Time 2 and feedback sign at Time 2 were added to the model as
predictors in Step 2. The results of this second step revealed a signiﬁcant main effect of
feedback sign (1 (306) = 9.00, n < .01) but no effect of state prove goal orientation (1
(306) = 1.03, p_ > .05). Further, the addition of an interaction term among state prove
goal orientation and feedback sign to the model in Step 3 did not result in a signiﬁcant
increment in R2 (A32 = .0023, E (1, 305) = 1.14, p > .05).

Finally, Step 1 of the third set of hierarchical regression analyses for prove goal
orientation consisted of subjects’ levels of joy at Time 3 as the criterion and their levels

of joy at Time 2 as the predictor (32 = .2980, E (1, 308) = 130.78, p < .01). Next, state

109

prove goal orientation at Time 3 and feedback sign at Time 3 were added to the model as
predictors in Step 2. The results of this second step revealed a signiﬁcant main effect of
feedback sign (1(306) = 7.37, n < .01) but no effect of state prove goal orientation Q
(306) = -0.29, p_ > .05). The addition of an interaction term among state prove goal
orientation and feedback sign to the model in Step 3 resulted in a signiﬁcant increment in
R2 (AB2 = .0160, E (l, 305) = 8.41, p_ < .01). As seen in Figure 10, with an increase in
state prove goal orientation positive feedback led to positive levels of joy and negative
feedback led to negative levels of joy, as hypothesized. Thus, the hypothesized
interaction between state prove goal orientation and feedback sign was observed at Time
3 but not at Times 1 or 2.

Avoid goal orientation. With respect to avoid goal orientation, Hypothesis 6
predicted that as individuals’ levels of state avoid goal orientation increased, positive
feedback would lead to positive levels of distress (i.e., relief) and negative feedback
would lead to negative levels of distress (i.e., anxiety). Step 1 of the ﬁrst set of
hierarchical regression analyses for avoid goal orientation consisted of subjects’ levels of
distress at Time 1 as the criterion, and state avoid goal orientation at Time 1 and feedback
sign at Time 1 as predictors (32 = .1860, E (2, 307) = 35.07, p < .01). The results of this
step revealed a signiﬁcant main effect of feedback sign (_t (307) = 8.37, p < .01) but no
effect of state avoid goal orientation (t (307) = -1.22, p_ > .05). Further, the addition of an
interaction term among these two variables to the model in Step 2 resulted in a signiﬁcant
increment in R2 (A32 = .0387, E (1, 306) = 15.29, p_ < .01). As hypothesized, increases in

state avoid goal orientation led to positive levels of distress when subjects received

110

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positive feedback and negative levels of distress when they received negative feedback
(see Figure 11).

Step 1 of the second set of hierarchical regression analyses for avoid goal
orientation consisted of subjects’ levels of distress at Time 2 as the criterion and their
levels of distress at Time 1 as the predictor (132 = .2806, E (1, 308) = 120.15, p_ < .01).
Next, state avoid goal orientation at Time 2 and feedback sign at Time 2 were added to
the model as predictors in Step 2. The results of this second step revealed a signiﬁcant
main effect of feedback sign (t (306) = 6.40, n < .01) but no effect of state avoid goal
orientation (t (306) = -1.79, n > .05). Further, the addition of an interaction term among
state avoid goal orientation and feedback sign to the model in Step 3 did not result in a
signiﬁcant increment in R2 (An2 = .0012, E (1, 305) = 0.59, p > .05).

Finally, Step 1 of the third set of hierarchical regression analyses for avoid goal
orientation consisted of subjects’ levels of distress at Time 3 as the criterion and their
levels of distress at Time 2 as the predictor (32 = .3943, E (1, 308) = 200.47, p < .01).
Next, state avoid goal orientation at Time 3 and feedback sign at Time 3 were added to
the model as predictors in Step 2. The results of this second step revealed a signiﬁcant
main effect of feedback sign (3 (306) = 4.38, p_ < .01) but no effect of state avoid goal
orientation (3 (306) = -1.42, p > .05). Similar to Time 1, the addition of an interaction
term among state avoid goal orientation and feedback sign to the model in Step 3 resulted
in a signiﬁcant increment in R2 (A32 = .0193, E (1, 305) = 10.76, p < .01). In support of
Hypothesis 6, increases in state avoid goal orientation led to positive levels of distress
when subjects received positive feedback and negative levels of distress when they

received negative feedback (see Figure 12). Thus, the hypothesized interaction between

113

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state avoid goal orientation and feedback sign was observed at Times 1, and 3 but not at
Time 2. Further, collapsing across all three goal orientations, Hypothesis 6 received
partial support.

Ir_ait Goal Orientations, Feedback Sign, and Affective Reactions

Similar to Hypothesis 6 with respect to state goal orientations, Hypothesis 7
predicted that trait goal orientations and feedback sign would interact in their effects on
individuals’ experience of affect. Again, three sets of hierarchical regression analyses
(i.e., one set each for Time 1, Time 2, and Time 3) were conducted with respect to trait
mastery, prove, and avoid goal orientations. Further, for Times 2 and 3, affective
reactions from Times 1 and 2 (respectively) were partialled before proceeding with the
analyses. The results of these analyses are described in detail below and are summarized
in Tables 15-17 for mastery, prove, and avoid goal orientations, respectively.

Mastery goal orientation. With respect to mastery goal orientation, Hypothesis 7
predicted that as individuals’ levels of trait mastery goal orientation increased, positive
feedback would lead to positive levels of joy (i.e., elation) and negative feedback would
lead to negative levels of joy (i.e., depression). Step 1 of the ﬁrst set of hierarchical
regression analyses for mastery goal orientation consisted of subjects’ levels of joy at
Time 1 as the criterion, and trait mastery goal orientation at Time 1 and feedback sign at
Time 1 as predictors (32 = .2287, E (2, 307) = 45.51, p < .01). The results of this step
revealed a signiﬁcant main effect of feedback sign (1 (307) = 9.53, p < .01) but no effect
of trait mastery goal orientation (1 (307) = 0.04, p > .05). Adding an interaction term
among these two variables to the model in Step 2 did not result in a signiﬁcant increment

in R2 (A32 = .0008, 15 (1, 306) = 0.34, p_ > .05).

115

Table 15

Summary of Hierarchical Reggssion Analyses for Trait Mastery Gcgl Orien

Sign, and the Experience of Jo;

tation Feedback

 

 

 

Model B R2 AR2 F

Time 1

Step 1 .2287 45.51**
Trait mastery goal orientation .002
Feedback sign .969**

Step 2 .2295 .0008 0.34
Trait mastery goal orientation * Feedback sign -.059

Time 2

Step 1 .2160 84.88“
Joy (Time 1) .464**

Step 2 .3863 .1703 42.46M
Trait mastery goal orientation .026
Feedback sign .865**

Step 3 .3865 .0002 0.10
Trait mastery goal orientation * Feedback sign .030

Time 3

Step 1 .2980 130.78“
Joy (Time 2) .544“

Step 2 .4036 .1056 27.09”
Trait mastery goal orientation -.003
Feedback sign .655M

Step 3 .4045 .0009 0.45
Trait mastery goal orientation * Feedback sign .059

 

*p<.05; **p<.01

116

Table 16

Summaﬂ of Hierarchicaﬂegression Analyses for Trait Prove Goal Oﬁengtion. Feedback

Sign, and the Experience of Joy

 

 

Model [5 R2 AR2 F

Time 1

Step 1 .2444 49.64“
Trait prove goal orientation -.126*
Feedback sign .972**

Step 2 .2446 .0002 0.10
Trait prove goal orientation * Feedback sign -.032

Time 2

Step 1 .2160 84.88**
Joy (Time 1) .464**

Step 2 .3879 .1719 42.97**
Trait prove goal orientation .048
Feedback sign .863**

Step 3 .3931 .0052 2.62
Trait prove goal orientation * Feedback sign .149

Time 3

Step 1 .2980 130.78“
Joy (Time 2) .544“

Step 2 .4062 .1082 27.88"
Trait prove goal orientation -.051
Feedback sign .650“

Step 3 .4129 .0067 3.46
Trait prove goal orientation * Feedback sign .164

 

*p<.05; **p<.01

117

Table 17

Summary of Hierarchical Regression Analyses for Trait Avoid Goal Orientation. Feedbagk

Sng, and the Experience of Distress

 

 

Model [3 R2 AR2 F

Time 1

Step 1 .1866 35.22**
Trait avoid goal orientation -.067
Feedback sign .864M

Step 2 .1933 .0067 2.53
Trait avoid goal orientation * Feedback sign .165

Time 2

Step 1 .2806 120.15**
Distress (Time 1) .531**

Step 2 .3711 .0905 22.02**
Trait avoid goal orientation -.100*
Feedback sign .609"

Step 3 .3716 .0005 0.25
Trait avoid goal orientation * Feedback sign -.048

Time 3

Step 1 .3943 200.47**
Distress (Time 2) .626M

Step 2 .4307 .0364 9.78**
Trait avoid goal orientation .026
Feedback sign .383**

Step 3 .4307 .0000 0.00
Trait avoid goal orientation * Feedback sign -.001

 

*p_<.05;**p<.01

118

Step 1 of the second set of hierarchical regression analyses for mastery goal
orientation consisted of subjects’ levels of joy at Time 2 as the criterion and their levels
of joy at Time 1 as the predictor (32 = .2160, 13(1, 308) = 84.88, 2 < .01). Next, trait
mastery goal orientation at Time 2 and feedback sign at Time 2 were added to the model
as predictors in Step 2. The results of this second step revealed a signiﬁcant main effect
of feedback sign (t (306) = 9.21, p < .01) but no effect of trait mastery goal orientation (1
(306) = 0.58, p > .05). Further, the addition of an interaction term among trait mastery
goal orientation and feedback sign to the model in Step 3 did not result in a signiﬁcant
increment in R2 (A3} = .0002, E (1, 305) = o. 10, 2 > .05).

Finally, Step 1 of the third set of hierarchical regression analyses for mastery goal
orientation consisted of subjects’ levels of joy at Time 3 as the criterion and their levels
of joy at Time 2 as the predictor (32 = .2980, E (1, 308) = 130.78, p < .01). Next, trait
mastery goal orientation at Time 3 and feedback sign at Time 3 were added to the model
as predictors in Step 2. The results of this second step revealed a signiﬁcant main effect
of feedback sign (t (306) = 7.36, p < .01) but no effect of trait mastery goal orientation (1
(306) = -0.08, p > .05). Again, the addition of an interaction term among trait mastery
goal orientation and feedback sign to the model in Step 3 did not result in a signiﬁcant
increment in R2 (AB2 = .0009, E (l, 305) = 0.45, p > .05). Thus, the hypothesized
interaction between trait mastery goal orientation and feedback sign was not observed at
Times 1, 2, or 3.

Prove goal orientation. Similar to trait mastery goal orientation, Hypothesis 7
predicted that as individuals’ levels of trait prove goal orientation increased, positive

feedback would lead to positive levels of joy (i.e., elation) and negative feedback would

119

lead to negative levels of joy (i.e., depression). Step 1 of the ﬁrst set of hierarchical
regression analyses for prove goal orientation consisted of subjects’ levels of joy at Time
1 as the criterion, and trait prove goal orientation at Time 1 and feedback sign at Time 1
as predictors (32 = .2444, E (2, 307) = 49.64, p_ < .01). The results of this step revealed
signiﬁcant main effects of trait prove goal orientation (t (307) = -2.53, p < .01) and
feedback sign (1 (307) = 9.67, p_ < .01 ). Adding an interaction term among these two
variables to the model in Step 2 did not result in a signiﬁcant increment in R2 (A132 =
.0002, E (l, 306) = 0.10, p_ > .05).

Step 1 of the second set of hierarchical regression analyses for prove goal
orientation consisted of subjects’ levels of joy at Time 2 as the criterion and their levels
of joy at Time 1 as the predictor (32 = .2160, E (l, 308) = 84.88, p < .01). Next, trait
prove goal orientation at Time 2 and feedback sign at Time 2 were added to the model as
predictors in Step 2. The results of this second step revealed a signiﬁcant main effect of
feedback sign (t (306) = 9.20, p < .01) but no effect of trait prove goal orientation (t (306)
= 1.07, p > .05). Further, the addition of an interaction term among trait prove goal
orientation and feedback sign to the model in Step 3 did not result in a signiﬁcant
increment in R2 (AB2 = .0052, E (1, 305) = 2.62, p > .05).

Finally, Step 1 of the third set of hierarchical regression analyses for prove goal
orientation consisted of subjects’ levels of joy at Time 3 as the criterion and their levels
of joy at Time 2 as the predictor (32 = .2980, E (1, 308) = 130.78, p < .01). Next, trait
prove goal orientation at Time 3 and feedback sign at Time 3 were added to the model as
predictors in Step 2. The results of this second step revealed a signiﬁcant main effect of

feedback sign (t (306) = 7.32, p < .01) but no effect of trait prove goal orientation (1 (306)

120

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= -1.15, p > .05). The addition of an interaction term among trait prove goal orientation
and feedback sign to the model in Step 3 resulted in a marginally signiﬁcant increment in
R2 (AB2 = .0067, E (1, 305) = 3.46, p_ = .0637). As seen in Figure 13, with an increase in
trait prove goal orientation positive feedback led to positive levels of joy and negative
feedback led to negative levels of joy, as hypothesized. Thus, the hypothesized
interaction between trait prove goal orientation and feedback sign was observed only at
Time 3 (i.e., marginally).

Avoid ggal orientm With respect to avoid goal orientation, Hypothesis 7
predicted that as individuals’ levels of trait avoid goal orientation increased, positive
feedback would lead to positive levels of distress (i.e., relief) and negative feedback
would lead to negative levels of distress (i.e., anxiety). Step 1 of the ﬁrst set of
hierarchical regression analyses for avoid goal orientation consisted of subjects’ levels of
distress at Time 1 as the criterion, and trait avoid goal orientation at Time 1 and feedback
sign at Time 1 as predictors (32 = .1866, E (2, 307) = 35.22, p_ < .01). The results of this
step revealed a signiﬁcant main effect of feedback sign (t (307) = 8.30, p < .01) but no
effect of trait avoid goal orientation (t (307) = -1.31, p > .05). Further, the addition of an
interaction term among these two variables to the model in Step 2 did not result in a
signiﬁcant increment in R2 (A32 = .0067, E. (l, 306) = 2.53, p > .05).

Step 1 of the second set of hierarchical regression analyses for avoid goal
orientation consisted of subjects’ levels of distress at Time 2 as the criterion and their
levels of distress at Time 1 as the predictor (32 = .2806, E (I, 308) = 120.15, p < .01).
Next, trait avoid goal orientation at Time 2 and feedback sign at Time 2 were added to
the model as predictors in Step 2. The results of this second step revealed signiﬁcant

0

122

main effects of trait avoid goal orientation sign (1 (306) = -2.17, p < .05) and feedback
sign (1 (306) = 6.36, p < .01). Further, the addition of an interaction term among trait
avoid goal orientation and feedback sign to the model in Step 3 did not result in a
signiﬁcant increment in R2 (AB2 = .0005, E (l, 305) = 0.25, p > .05).

Finally, Step 1 of the third set of hierarchical regression analyses for avoid goal
orientation consisted of subjects’ levels of distress at Time 3 as the criterion and their
levels of distress at Time 2 as the predictor (32 = .3943, E (l, 308) = 200.47, p < .01).
Next, trait avoid goal orientation at Time 3 and feedback sign at Time 3 were added to
the model as predictors in Step 2. The results of this second step revealed a signiﬁcant
main effect of feedback sign (1(306) = 4.37, p < .01) but no effect of trait avoid goal
orientation (1(306) = 0.61, p_ > .05). Similar to Time 1, the addition of an interaction
term among trait avoid goal orientation and feedback sign to the model in Step 3 did not
result in a signiﬁcant increment in R2 (AB2 = .0000, E (1, 305) = 0.00, p > .05). Thus, the
hypothesized interaction between trait avoid goal orientation and feedback sign was not
observed at Times 1, 2, or 3. Further, collapsing across all three goal orientations,
Hypothesis 7 received only partial support (i.e., the marginally signiﬁcant interaction
between trait prove goal orientation and feedback sign at Time 3).

Performance Evaluations and Affective Reactions

Hypothesis 8 predicted that individuals’ performance evaluations would have a
main effect on affective reactions. Speciﬁcally, it was predicted that positive
performance evaluation (i.e., evaluations of success) would lead to positive levels of joy
(i.e., elation) and distress (i.e., relief) whereas negative performance evaluations (i.e.,

evaluations of failure) would lead to negative levels of joy (i.e., depression) and distress

123

(i.e., anxiety). For Times 2 and 3, affective reactions from Times 1 and 2 (respectively)
were partialled; thus, results reported with respect to Hypothesis 8 are based on Type I
SS.

lg, As expected, performance evaluations at Time 1 were positively related to
joy at Time 1 Q = .73, p < .01). To test for this relationship at Time 2, joy at Time 2 was
entered into a regression analysis as the criterion, and joy at Time 1 and performance
evaluations at Time 2 were entered as predictors. Results revealed that after partialling
the effects of joy at Time 1 Q (307) = 13.18, p_ < .01), performance evaluations at Time 2
had a signiﬁcant main effect on joy at Time 2 Q (307) = 17.95, p_ < .01). Finally, joy at
Time 3 was entered into a regression analysis as the criterion, and joy at Time 2 and
performance evaluations at Time 3 were entered as predictors. As expected, results
revealed that after partialling the effects of joy at Time 2 Q (307) = 14.28, p_ < .01),
performance evaluations at Time 3 had a signiﬁcant main effect on joy at Time 3 Q (307)
= 13.16, p_ < .01).

Distress. Also as expected, performance evaluations at Time 1 were positively

 

related to distress at Time 1 Q = .61, p < .01). Again, to test for this relationship at Time
2, distress at Time 2 was entered into a regression analysis as the criterion, and distress at
Time 1 and performance evaluations at Time 2 were entered as predictors. Results
revealed that after partialling the effects of distress at Time 1 Q (307) = 12.88, p < .01),
performance evaluations at Time 2 had a signiﬁcant main effect on distress at Time 2 Q
(307) = 10.87, p < .01). Finally, distress at Time 3 was entered into a regression analysis
as the criterion, and distress at Time 2 and performance evaluations at Time 3 were

entered as predictors. Again, results revealed that after partialling the effects of distress

124

at Time 2 Q (307) = 15.53, p < .01), performance evaluations at Time 3 had a signiﬁcant
main effect on distress at Time 3 Q (307) = 7.97, p < .01). Thus, Hypothesis 8 received
full support.
Performance Evaluations and Subsequent Self-Efﬁcacy

Hypothesis 9 predicted that individuals’ performance evaluations would have a
main effect on their subsequent (i.e., Times 2-4) self-efﬁcacy. Speciﬁcally, Hypothesis 9
predicted that positive performance evaluation (i.e., evaluations of success) would lead to
high levels of self-efﬁcacy whereas negative performance evaluations (i.e., evaluations of
failure) would lead to low levels of self-efﬁcacy. Self-efﬁcacy from the previous
iteration of the cycle was partialled before examining the hypothesized relationship at
Times 2-4; thus, results reported with respect to Hypothesis 9 are based on Type I SS.

First, self-efﬁcacy at Time 2 was entered into a regression analysis as the
criterion, and self-efﬁcacy at Time 1 and performance evaluations at Time 1 were entered
as predictors. Results revealed that after partialling the effects of self-efﬁcacy at Time 1
Q (307) = 14.24, p < .01), performance evaluations at Time 1 had a signiﬁcant main
effect on self-efﬁcacy at Time 2 Q (307) = 14.65, p < .01). Next, self-efﬁcacy at Time 3
was entered into a regression analysis as the criterion, and self-efﬁcacy at Time 2 and
performance evaluations at Time 2 were entered as predictors. As expected, results
revealed that after partialling the effects of self—efﬁcacy at Time 2 Q (307) = 26.05, p_ <
.01), performance evaluations at Time 2 had a signiﬁcant main effect on self-efﬁcacy at
Time 3 Q (307) = 10.92, p < .01). Finally, self-efﬁcacy at Time 4 was entered into a
regression analysis as the criterion, and self-efﬁcacy at Time 3 and performance

evaluations at Time 3 were entered as predictors. As hypothesized, results revealed that

125

after partialling the effects of self-efﬁcacy at Time 3 Q (307) = 30.53, p < .01),
performance evaluations at Time 3 had a signiﬁcant main effect on self-efﬁcacy at Time
4 Q (307) = 9.30, p < .01). Thus, Hypothesis 9 received full support.

Affective Reactions and Subsequent State Goal Orientartigaa

Masteg goal orientation. With respect to mastery goal orientation, Hypothesis 11
predicted that increases in affective reactions of joy (i.e., elation) would lead to increases
in subsequent state mastery goal orientation. To test this relationship at Time 2 in the
cycle, state mastery goal orientation at Time 2 was entered into a regression analysis as
the criterion, and state mastery goal orientation at Time 1 and joy at Time 1 were entered
as predictors. After partialling the effects of state mastery goal orientation at Time 1 Q
(307) = 13.64, p_ < .01), joy at Time 1 had a main effect on state mastery goal orientation
at Time 2 Q (307) = 2.51, p < .05; Type I SS).

Next, state mastery goal orientation at Time 3 was entered into a regression
analysis as the criterion, and state mastery goal orientation at Time 2 and joy at Time 2
were entered as predictors. After partialling the effects of state mastery goal orientation
at Time 2 Q (307) = 19.09, p < .01), joy at Time 2 had a main effect on state mastery goal
orientation at Time 3 Q (307) = 3.94, p < .01; Type I SS).

Finally, state mastery goal orientation at Time 4 was entered into a regression
analysis as the criterion, and state mastery goal orientation at Time 3 and joy at Time 3
were entered as predictors. After partialling the effects of state mastery goal orientation
at Time 3 Q (307) = 24.89, p < .01), joy at Time 3 had a main effect on state mastery goal

orientation at Time 4 Q (307) = 4.63, p_ < .01; Type I SS). Thus, the hypothesized

126

relationship between joy and subsequent state mastery goal orientation was observed at
all three time periods.

Prove goal orientation. Similar to mastery goal orientation, Hypothesis 11
predicted that increases in affective reactions of joy (i.e., elation) would lead to increases
in subsequent state prove goal orientation. To test this relationship, state prove goal
orientation at Time 2 was entered into a regression analysis as the criterion, and state
prove goal orientation at Time 1 and joy at Time 1 were entered as predictors. After

partialling the effects of state prove goal orientation at Time 1 Q (307) = 17.34, p_ < .01),

 

joy at Time 1 did not have a main effect on state prove goal orientation at Time 2 Q (307) I
=1.61, p > .05; Type I SS).

Next, state prove goal orientation at Time 3 was entered into a regression analysis
as the criterion, and state prove goal orientation at Time 2 and joy at Time 2 were entered
as predictors. After partialling the effects of state prove goal orientation at Time 2 Q
(307) = 22.24, p < .01), joy at Time 2 did not have a main effect on state prove goal
orientation at Time 3 Q (307) = 0.90, p_ > .05; Type I SS).

Finally, state prove goal orientation at Time 4 was entered into a regression
analysis as the criterion, and state prove goal orientation at Time 3 and joy at Time 3
were entered as predictors. Again, after partialling the effects of state prove goal
orientation at Time 3 Q (307) = 23.91, p < .01), joy at Time 3 did not have a main effect
on state prove goal orientation at Time 4 Q (307) = -0.93, p_ > .05; Type I SS). Thus, the
hypothesized relationship between joy and subsequent state prove goal orientation was

not observed at any of the three time periods.

127

Hypothesis 11 also predicted that increases in affective reactions of distress (i.e.,
relief) would lead to increases in subsequent state prove goal orientation. To test this
relationship at Time 2, state prove goal orientation at Time 2 was entered into a
regression analysis as the criterion, and state prove goal orientation at Time 1 and distress
at Time 1 were entered as predictors. After partialling the effects of state prove goal
orientation at Time 1 Q (307) = 17.29, p_ < .01), distress at Time 1 did not have a main
effect on state prove goal orientation at Time 2 Q (307) = 0.92, p > .05; Type I SS).

Next, state prove goal orientation at Time 3 was entered into a regression analysis
as the criterion, and state prove goal orientation at Time 2 and distress at Time 2 were
entered as predictors. After partialling the effects of state prove goal orientation at Time
2 Q (307) = 22.23, p < .01), distress at Time 2 did not have a main effect on state prove
goal orientation at Time 3 Q (307) = 0.62, p > .05; Type I SS).

Finally, state prove goal orientation at Time 4 was entered into a regression
analysis as the criterion, and state prove goal orientation at Time 3 and distress at Time 3
were entered as predictors. Again, after partialling the effects of state prove goal
orientation at Time 3 Q (307) = 24.02, p < .01), distress at Time 3 did not have a main
effect on state prove goal orientation at Time 4 Q (307) = -l.96, p > .05; Type I SS).
Thus, similar to joy, the hypothesized relationship between distress and subsequent state
prove goal orientation was not observed at any of the three time periods.

Avoid goal orientation. With respect to avoid goal orientation, Hypothesis 11
predicted that decreases in affective reactions of joy (i.e., depression) would lead to
increases in subsequent state avoid goal orientation. To test this relationship at Time 2 in

the cycle, state avoid goal orientation at Time 2 was entered into a regression analysis as

128

the criterion, and state avoid goal orientation at Time 1 and joy at Time 1 were entered as
predictors. After partialling the effects of state avoid goal orientation at Time 1 Q (307) =
20.48, p < .01), joy at Time 1 did not have a main effect on state avoid goal orientation at
Time 2 Q (307) = 0.26, p > .05; Type I SS).

Next, state avoid goal orientation at Time 3 was entered into a regression analysis
as the criterion, and state avoid goal orientation at Time 2 and joy at Time 2 were entered
as predictors. After partialling the effects of state avoid goal orientation at Time 2 Q
(307) = 25.28, p < .01), joy at Time 2 had a main effect on state avoid goal orientation at
Time 3 Q (307) = -2.51, p < .05; Type I SS).

Finally, state avoid goal orientation at Time 4 was entered into a regression
analysis as the criterion, and state avoid goal orientation at Time 3 and joy at Time 3
were entered as predictors. After partialling the effects of state avoid goal orientation at
Time 3 Q (307) = 28.90, p < .01), joy at Time 3 did not have a main effect on state avoid
goal orientation at Time 4 Q (307) = -0.63, p < .01; Type I SS). Thus, the hypothesized
relationship between joy and subsequent state avoid goal orientation was observed only at
Time 3.

Hypothesis 11 also predicted that decreases in affective reactions of distress (i.e.,
anxiety) would lead to increases in subsequent state avoid goal orientation. To test this
relationship, state avoid goal orientation at Time 2 was entered into a regression analysis
as the criterion, and state avoid goal orientation at Time 1 and distress at Time 1 were
entered as predictors. After partialling the effects of state avoid goal orientation at Time
1 Q (307) = 20.55, p < .01), distress at Time 1 did not have a main effect on state avoid

goal orientation at Time 2 Q (307) = -l.38, p > .05; Type I SS).

129

Next, state avoid goal orientation at Time 3 was entered into a regression analysis
as the criterion, and state avoid goal orientation at Time 2 and distress at Time 2 were
entered as predictors. After partialling the effects of state avoid goal orientation at Time
2 Q (307) = 25.39, p < .01), distress at Time 2 had a main effect on state avoid goal
orientation at Time 3 Q (307) = -3.04, p_ < .01; Type I SS).

Finally, state avoid goal orientation at Time 4 was entered into a regression
analysis as the criterion, and state avoid goal orientation at Time 3 and distress at Time 3
were entered as predictors. Again, after partialling the effects of state avoid goal
orientation at Time 3 Q (307) = 28.99, p < .01), distress at Time 3 did not have a main
effect on state avoid goal orientation at Time 4 Q (307) = -l.50, p_ > .05; Type I SS).
Thus, the hypothesized relationship between distress and subsequent state avoid goal
orientation was observed only at Time 2. Further, collapsing across the three goal
orientations, Hypothesis 11 received partial support.

Samara; of Results

Figure 14 summarizes the linkages that were supported consistently (but not
necessarily fully) in the current study. Some of the relationships in the ﬁgure were
supported each time they were assessed (e. g., positive relationship between performance
evaluations and subsequent self-efﬁcacy) whereas other relationships were supported
only sometimes (e. g., positive relationship between affect and subsequent state goal
orientations was only observed between affective reactions of joy and state mastery goal
orientation, but this relationship was observed at each time period).

Predictors of initial state goal orientations. As noted above, the hypothesized

interactions between trait goal orientations and situational factors (i.e., goal orientation

130

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manipulation) as well as between trait goal orientations and individual differences (i.e.,
openness to experiences, agreeableness, self-monitoring) on initial state goal orientations
were not supported. In fact, the only consistent predictors of subjects’ initial state goal
orientations were their trait goal orientations. Subjects’ pre-task self-efﬁcacy was also a
signiﬁcant predictor of their initial state mastery and prove goal orientations but only a
marginally signiﬁcant predictor of subjects’ initial state avoid goal orientation.

Effects on mrformance evaluations and affective reaction; In all three waves of
data, positive feedback was associated with performance evaluations of success and
affective reactions of elation (i.e., positive joy) and relief (i.e., positive distress), and
negative feedback was associated with performance evaluations of failure and affective
reactions of depression (i.e., negative joy) and anxiety (i.e., negative distress). Further,
the relationship between feedback sign and performance evaluations was moderated by
trait prove and avoid goal orientations fairly consistently across the three waves of data.
Speciﬁcally, as subjects’ levels of trait prove and avoid goal orientations increased,
positive feedback was associated with performance evaluations of success and negative
feedback was associated with performance evaluations of failure.

Additionally, the relationship between feedback sign and affective reactions was
moderated by state avoid goal orientations fairly consistently across the three waves of
data. Speciﬁcally, as subjects levels of state avoid goal orientations increased, positive
feedback was associated with positive levels of distress (i.e., relief) and negative
feedback was associated with negative levels of distress (i.e., anxiety). The moderating
effects of state goal orientations on the relationship between feedback sign and affective

reactions was also observed for state prove goal orientation at Wave 3 (i.e., as state prove

132

goal orientation increased, positive feedback was associated with positive levels of joy
and negative feedback was associated with negative levels of joy). However, this effect
was not as consistent as the aforementioned moderating effect of state avoid goal
orientation.

Finally, the hypothesized relationship between performance evaluations and affect
was observed at all three waves of data (see Figure 14). That is, performance evaluations
of success (i.e., positive performance evaluations) were associated with affective
reactions of elation (i.e., positive joy) and relief (i.e., positive distress). In contrast,
performance evaluations of failure (i.e., negative performance evaluations) were
associated with affective reactions of depression (i.e., negative joy) and anxiety (i.e.,
negative distress).

Self-efﬁcacy revisited. As shown in Figure 14, the hypothesized relationship
between performance evaluations and subsequent self—efﬁcacy was observed in all three
waves of data. Speciﬁcally, performance evaluations of success were associated with
high levels of subsequent self-efﬁcacy, and performance evaluations of failure were
associated with low levels of subsequent self—efﬁcacy. Further, as noted earlier, pre-task
self-efﬁcacy was related to initial state goal orientations (only marginally signiﬁcant with
respect to state avoid goal orientation). Likewise, self-efﬁcacy at later points in the study
were also associated with subsequent state goal orientations. However, as with initial
state goal orientations, self-efﬁcacy was signiﬁcantly related only to subsequent state
mastery and prove goal orientations.

Affective reactions and subsmuent state goal orientations. The hypothesized

linkage from affective reactions to subsequent state goal orientations received only partial

133

 

support. First, the proposed relationship between affective reactions of joy and
subsequent state mastery goal orientation was observed in all three waves of data.
Speciﬁcally, affective reactions of elation (i.e., positive joy) were associated with high
levels of subsequent state mastery goal orientation, and affective reactions of depression
(i.e., negative distress) were associated with low levels of subsequent state mastery goal
orientation.

Second, the hypothesized relationship between affective reactions and subsequent
state avoid goal orientation was also observed (although, not as consistently as state
mastery goal orientation). For example, as affective reactions of depression (i.e.,
negative joy) increased, subsequent state avoid goal orientation increased; this
relationship, however, was observed only at Time 3. Similarly, as affective reactions of
anxiety (i.e., negative distress) increased, subsequent state avoid goal orientation
increased; again, this relationship was observed only at one time period (i.e., Time 2).
Regardless, the current data provide some support of the hypothesized linkage from

affective reactions to subsequent state goal orientations.

134

DISCUSSION
Summm of Research Goals

The goals and questions of interest in this study fall primarily into two categories.
The ﬁrst area of interest was an examination of potential factors that may lead to
individuals’ initial state goal orientations when they encounter a situation. As shown in
the conceptual and operational models (Figures 1 and 2, respectively), these potential
factors include 1) individuals’ levels of trait goal orientations, 2) situational cues or
inﬂuences, 3) other personality variables that might represent the malleability of an
individual’s behavior and motivation, and 4) self-efﬁcacy (based on any information that
individuals may have about the situation).

The second area of interest was an examination of changes that occur to
individuals’ state goal orientations as they progress through time and gain experience at a
given task. Of particular interest in the current study was how individuals’ state goal
orientations change after they receive feedback regarding their progress and performance
on a problem-solving task. Understanding how different individuals interpret and
respond to positive and negative performance feedback (and how these responses
translate into future motivation) is a fruitful area of research.

Review of Goal 1 Attainment

The lack of effects of the goal orientation manipulation on any of the state goal
orientation scales at any of the time periods suggests that it may have failed to alter
individuals’ attitudes and motivation related to performance on the task. This is
surprising considering that the current manipulation was developed by combining

elements of previous successful manipulations. For example, on a computerized version

135

of the problem-solving board game MAS’I‘ERMIND, Chambers et al. (2000) induced a
mastery goal orientation by telling subjects that the game was “an opportunity for them to
improve [their] logical reasoning skills” and a performance goal orientation by telling
subjects that the game was “an opportunity to demonstrate [their] logical reasoning skills.
Having subjects solve a series of word-ﬁnd puzzles, Elliot and Harachiewicz (1996)
induced a prove goal orientation by telling subjects that “some students stand out because IF
they do quite well on the puzzles” and an avoid goal orientation by telling subjects that
“some students stand out because they do quite poorly on the puzzles.” Using a simple

pattern recognition task, Elliott and Dweck (1988) induced a mastery goal orientation by

 

telling subjects “you’ll probably learn a lot of new things. . .but you’ll probably make a
bunch of mistakes” and a performance goal orientation by telling subjects “although you
won’t learn new things, it will really show me what kids can do.” Further, in a 3-hour
computer training course, Martocchio (1994) induced a mastery goal orientation by
telling subjects to “remember that the more practice you have, the more capable you will
become” and a performance goal orientation by telling subjects to “remember that
learning how to use microcomputers is based on skills that you already possess?’ Finally,
using a computerized class scheduling task, Steele-Johnson et al. (2000) induced a
mastery goal orientation by telling subjects that “skills on problem solving tasks like the
class scheduling task are developed through practice. . .the more people practice, the more
capable they become. . .you can expect to make some mistakes. . .you should see your
performance improve with practice” and a performance goal orientation by telling

subjects that “performance on problem solving tasks like the class scheduling task

136

reﬂects basic cognitive capabilities. . .The higher their underlying cognitive capacities, the
better is their problem solving.”

A close examination of the aforementioned goal orientation manipulations and the
manipulations employed in the current study reveal great similarities—the current
manipulations were developed based on these previous manipulations. However, in
developing the current manipulations, key phrases were taken from the aforementioned
studies and combined to form a “powerful” manipulation. In doing so, however, it is
possible that subjects became confused because they were not given enough information
to understand the manipulations. For example, subjects in the prove and avoid conditions
were told “remember, you already have the ability.” What does this mean? Is this
enough for subjects to get the idea that their ability is ﬁxed and practicing will not lead to
increased competence and performance? Similarly, consider the mastery manipulation,
in which subjects were ﬁrst told that “we can learn from our mistakes” and later told to
“focus [their] attention on developing [their] skills and mastering the task rather than on
the mistakes that [they] make.” These two phrases are inconsistent. Hearing the second
phrase may have led subjects to think “but you just told me that I can learn from my
mistakes and now you’re telling me to ignore my mistakes. . .how am I supposed to
learn?” Thus, although the current manipulations were developed with the intent of
“hitting subjects over the head” with powerful words and phrases, it is quite possible that
subjects were instead confused and bafﬂed how they should translate the manipulations
into actions. Post—experiment interviews with subjects could provide insight regarding
the extent to which’this explanation is plausible--i.e., the extent to which subjects did or

did not construe the situation in a similar fashion based on the task instructions.

137

 

Unfortunately, such a strategy is not possible for the current study; however, future
researchers may wish to take this advice. To the extent that individuals construe the
situation differently, the manipulation has failed.

A second potential reason for this manipulation failure could be that the
manipulations were too powerful (rather than not powerful enough as the previous
explanation proposed) and thus unbelievable—Le, they were transparent. For example, *
the prove and avoid manipulations stressed to participants that they “already have the

ability.” Considering that college students served as subjects in the current study and

 

throughout college individuals are taught that they can learn new things and that studying
leads to better grades, subjects in these conditions may not have believed what they were
being told.

A third potential reason for the manipulation failure could be that two of the three
conditions were extremely similar to each other. The only difference between the prove
and avoid manipulations was “Some students really stand out because they do so much
better than everyone else; if you perform better than most other students do, this would
indicate that you have more ability than they do” (prove) vs. “Some students really stand
out because they do so much worse than everyone else; if you perform worse than most
other students do, this would indicate that you have less ability than they do” (avoid).
This difference may not have been enough to elicit a difference in motivational attitudes.
That is, subjects in the two conditions may have perceived these sentences as the same.

The model displayed in Figure 14 illustrates the linkages that were supported in
the current study. As discussed, the goal orientation manipulation has been removed

from this model since it did not have any main or interactive (with trait goal orientations)

138

effects on individuals’ initial state goal orientations. Also not included in the revised
model are the three personality variables (openness to experiences, extraversion, and self-
monitoring). Similar to the goal orientation manipulation, none of these variables had
their hypothesized interactive (with trait goal orientations) effects on individuals’ initial
state goal orientations. The lack of support for these hypothesized effects is not
necessarily inconsistent with the bulk of the theory discussed and tested in the current
study—in fact, there is no known empirical support for these effects. However, the lack
of effects associated with the goal orientation manipulation is surprising, as noted.
Previous research has consistently been able to inﬂuence individuals’ motivational beliefs
and attitudes through the use of task instructions. Thus, the model shown in Figure 14 is
not necessarily a proposed model to be tested with future research. Instead, it simply
highlights the supported linkages and suggests some relationships that future research
may seek to replicate.

The ﬁnal linkage shown in Figure 14 relevant to the discussion of predictors of
initial state goal orientations is self-efﬁcacy. As noted above, the relationship between
pre-task self-efﬁcacy and initial state goal orientations was observed with respect to state
mastery and prove goal orientations (the relationship was marginal with respect to state
avoid goal orientation). Thus, individuals’ trait goal orientations and pre-task self-
efﬁcacy were the best predictors of their initial state goal orientations.

Review of Goal 2 Attainment

Contrary to the lack of effects associated with the goal orientation manipulation,

the feedback manipulation yielded many hypothesized and theoretically sound effects.

For example, it is not surprising that positive feedback was associated with positive affect

139

and performance evaluations of success (and vice versa). Nor is it surprising that these
performance evaluations were associated with subsequent self-efﬁcacy, such that more
positive performance evaluations were associated with higher levels of self-efﬁcacy and
more negative performance evaluations were associated with lower levels of self-
efﬁcacy.

The results regarding subsequent state goal orientations, however, are not as clear.
Similar to initial state goal orientations, subsequent state goal orientations were predicted,
in part, by self-efﬁcacy. Speciﬁcally, there was a positive relationship between self-
efﬁcacy and subsequent state mastery and prove goal orientations—Le, as self-efﬁcacy
increased, state mastery and prove goal orientations also increased. There was no
relationship, however, between self—efﬁcacy and subsequent state avoid goal orientation.
It was hypothesized that as individuals’ self-efﬁcacy beliefs increased (i.e., individuals
thought that they could meet the demands of the task), their attention would shift away
from trying to avoid failure (i.e., state avoid goal orientation would decrease). However,
such patterns of results were not observed.

With regard to the relationship between affect and subsequent state goal
orientations, the only consistent ﬁnding observed in the current study was between the
experience of joy and subsequent state mastery goal orientation. Speciﬁcally, more
positive levels of joy (i.e., elation) were associated with higher levels of subsequent state
mastery goal orientation. It was hypothesized that the experience of both joy and distress
would be positively related to subsequent state prove goal orientation, such that more
positive levels of joy (i.e., elation) and distress (i.e., relief) would be associated with

higher levels of subsequent state prove goal orientation. Neither joy nor distress was

140

associated with subsequent state prove goal orientation. Finally, similar hypotheses
predicted that the exact opposite relationships would exist with respect to subsequent
state avoid goal orientation, such that more negative levels of joy (i.e., depression) and
distress (i.e., anxiety) would be associated with higher levels of subsequent state avoid
goal orientation. This relationship was observed for affective experiences of joy at Time
3 and distress at Time 2.

As seen in the revised model shown in Figure 14 (and the conceptual and
operational models shown in Figures 1 and 2, respectively), the only direct antecedents of
subsequent state goal orientations are self—efﬁcacy and affect (and previous state goal
orientations, which were treated as covariates in all of the analyses). Further, as was just
discussed, neither self-efﬁcacy nor affect were reliable predictors of subsequent state
avoid goal orientations. Moreover, self-efﬁcacy was the only reliable predictor of
subsequent state prove goal orientation, and this effect was, as hypothesized, identical to
the effect on subsequent state mastery goal orientation. Thus, because the effects of
affect on subsequent state prove goal orientation were not observed, the current model
cannot account for differences between subsequent state mastery and prove goal
orientations. Such a shortcoming warrants future research.

An additional area of ambiguity in the current model lies in the interactive effects
of goal orientations and feedback on performance evaluations and affect. It was
hypothesized that both state and trait goal orientations would interact with feedback (i.e.,
positive vs. negative) in their effects on performance evaluations and affect. Instead, trait
goal orientations had a fairly reliable moderating effect only on performance evaluations.

Likewise was true for state goal orientations with respect to affect. Neither of these

141

dependent variables should be any more susceptible to the effects of state or trait goal
orientations than the other. Such inconsistencies in results suggest that perhaps the
measures developed to assess performance evaluations and affect in the current study
tapped into constructs other than those intended. Again, these results warrant additional
research.

Thus, with regard to the second primary goal of the current study, state goal

alum

orientations did in fact change over time; however, the current data do not provide a clear ,
explanation of why they changed over time. Not only is it important to assess whether

state goal orientations change as a function of time (and possibly performance feedback),

 

but identifying the processes through which these changes occur is a useful endeavor.
With such information, we can better predict how different individuals may behave on
the job when they are subjected to varying conditions.
Future Directions

Even after conducting the current study, an important question still remains: What
determines an individual’s state goal orientation when he/she ﬁrst encounters a situation?
The strongest predictors of individuals’ initial state goal orientations in the current study
were their trait goal orientations. Trait mastery, prove, and avoid goal orientations
accounted for 14.5%, 27.3%, and 24.1% of the variance in initial state mastery, prove,
and avoid goal orientations (respectively). This should be no surprise—the best predictor
of future behavior is past behavior. However, even after adding self-efﬁcacy as a
predictor, only 19.2%, 29.1%, and 24.2% of the variance in initial state mastery, prove,
and avoid goal orientations (respectively) was explained. This leaves approximately 81%

of the variance in initial state mastery goal orientation, 71% of the variance in initial state

142

prove goal orientation, and 76% of the variance in initial state avoid goal orientation
unexplained. Most likely, random and measurement error do not account for all of this
unexplained variance.

The current study examined the potential inﬂuences of situational factors and
other individual difference variables on individuals’ initial state goal orientations.
Although none of these variables had any impact on state goal orientations, question has F
been raised regarding the adequacy of the goal orientation manipulation (i.e., situational -

inﬂuences). As noted, previous research (e. g., Chambers et al., 2000; Elliot &

 

Harackiewicz, 1996; Elliott & Dweck, 1988; Jagacinski & Nicholls, 1984; Koestner &
Zuckerman, 1994; Martocchio, 1994; Steele-Johnson et al., 2000) has successfully
manipulated individual’s state goal orientations through the use of task instructions very
similar to those used in the current study.

One reason for the manipulation failure in the current study, as suggested
previously, could be that subjects did not believe the task instructions because they were
transparent—i.e., they realized that the instructions were bogus. It is quite possible that
subjects enter psychology experiments with the expectation that they will be lied to or
manipulated, and are thus skeptical of everything that they see and hear throughout the
experiment.

To avoid the transparency of verbal manipulations, researchers should explore the
use of using more subtle, indirect means of manipulating state goal orientations, such as
performance feedback and the task environment itself. For example, providing
individuals with feedback that compares themselves to other individuals could be used to

elicit high levels of state prove or avoid goal orientations. In contrast, performance

143

feedback that informs subjects where and how they are improving compared to previous
performance could be used to elicit high levels of state mastery goal orientation.
Similarly, constructing the task environment as a competition among members could be
used to elicit high levels of state prove or avoid goal orientations, whereas constructing
the task environment as a cooperative atmosphere that facilitates and condones learning
could be used to elicit high levels of state mastery goal orientation. Manipulations such
as these have been successful in previous research (e. g., feedback: (Butler, 1988); task
environment: (Butler, 1989; Roberts, 1992)), but they have not been used as frequently as
task instructions.

A second area that future research should explore, as highlighted above, is the
process by which state goal orientations change over time. In the current study, state goal
orientations did indeed change, but these changes were not fully explained. For example,
perhaps the inﬂuence of individuals’ interest in the task should be explored. Fisher
(Fisher, 1998) found that individuals’ interest in an academic course had a main effect on
their state mastery goal orientatiOn (regarding the course) at the beginning of the
semester, but a moderating effect (with trait mastery goal orientation) toward the end of
the semester. No speciﬁc hypotheses or propositions are offered at this time, but the
roles of interest and other variables in the dynamic processes of state goal orientations
should be explored.

Finally, there exists informal debate among members of the research community
regarding whether individuals can be motivated by multiple state goal orientations at the
same time. Some argue that at a speciﬁc moment in time, an individual can be working

toward only one goal whereas others argue that it is possible for an individual to be

144

working toward multiple goals at the same time. Support for the former argument would
be strong negative correlations between state goal orientations measured at the same
point in time—if an individual is high on one dimension, he/she will be low on the others.
In contrast, support for the latter argument would exist if correlations between state goal
orientations measured at the same point in time were near zero—an individual’s level of
one state goal orientation does not necessitate any speciﬁc level of other state goal
orientations. In the current study, intercorrelations among state goal orientations
measured at the same point in time ranged from -.O4 to .72 (M = .30). Additional
research should be conducted to explore these competing hypotheses.
Limitations

Related to the last suggested direction for future research, a measure of state goal
orientations was developed speciﬁcally for this study, yet researchers do not completely
understand the nature of state goal orientations. Additionally, we do not yet know what a
good measure of state goal orientations should look like. Should it mimic measures of
trait goal orientations, changing only the point of reference to “in this situation” or “on
this task”? Should it be measured while the individual is engaged in the situation or task
rather than after he/she completes a wave or block? Without answers to questions such
as these, researchers cannot accept results of their state goal orientations measures as fact.
Thus, the measure of state goal orientations developed for the current study may not have
yielded an accurate assessment of state goal orientations.

A second limitation of the current study is that the task may have been too
difﬁcult. For example the MCPL task in the current study consisted of ﬁve cues; most

MCPL research uses two (DeShon & Alexander, 1996; O'Connor, Doherty, & Tweney,

145

1989) or three cues (Barley, Connolly, & Ekegren, 1989; Earley, Connolly, & Lee, 1989;
Taylor, Hall, Cosier, & Goodwin, 1996). Across the three trial blocks, performance (i.e.,
average prediction error) did not change (E (2, 618) = 1.10, p > .05); thus, individuals did
not improve with practice. Additionally, subjects’ levels of self-efﬁcacy actually
decreased throughout the experiment E (3, 927) = 96.24, p_ < .01). Thus, there is reason
to believe that subjects eventually gave up at the task. One reason for this could be that
subjects did not believe there was actually a formula used to predict the stock prices. In
fact, many subjects upon completing the experiment said, “Once I realized that the stock
prices were random, Ijust started guessing and then I did better!” Despite telling subjects
to ignore any previous information that they may already know about the stock market,
subjects may have concluded that stock prices in the current study were random when
prices (and algorithms) appeared inconsistent with what they already knew.

Finally, conducting multiple univariate analyses assumes that the dependent
variables are independent. To the extent that this is not true (i.e., dependent variables are
correlated), Type I error increases (T abachnick & Fidell, 2001). In the current study,
state goal orientations (i.e., the primary dependent variables of interest) at Time 1 ranged
from -.04 to .65, state goal orientations at Time 2 ranged from 0.00 to .65, state goal
orientations at Time 3 ranged from .09 to .68, and state goal orientations at Time 4
ranged from .10 to .72. This lack of independence among dependent variables poses a
limitation to the interpretation of univariate results.

Implications and Conclusion
In a variety of work settings, individuals’ state goal orientations can have drastic

implications for job performance. For example, high levels avoid goal orientation are

146

crucial for workers on a nuclear submarine—mistakes can be deadly. In contrast, high
levels of mastery goal orientation are desirable for jobs in which learning and adapting
are important. The current study found that giving individuals negative feedback
regarding their performance leads them to evaluate their performance as a failure and
experience negative affect. These negative performance evaluations and affect, in turn,
lead individuals to adopt lower levels of state mastery goal orientation in the future (the
effects of performance evaluations are mediated by self-efﬁcacy). This suggests that
managers, when providing their subordinates with negative feedback, should frame it in a
positive light; this would likely reduce the extent to which individuals interpret the
feedback as an indication of failure and experience negative affect.

In addition, the current study found that individuals’ trait goal orientations
continue to affect state goal orientations and their inherent processes even after initial
exposure to a task. Speciﬁcally, individuals’ trait mastery goal orientation moderates the
extent to which individuals interpret positive and negative feedback as an indication of
success vs. failure. Again, these performance evaluations have effects on subsequent
state mastery goal orientation (through self-efﬁcacy). This suggests that managers cannot
assume that they can simply mold their employees to possess high levels of mastery goal
orientation—individuals’ traits continue to inﬂuence their task-speciﬁc motivation even
after exposure to the task. Thus, incorporating trait goal orientations into the selection
context may prove useful for managers who desire for their employees to approach their
jobs with a focus on mastering their work and learning to adapt to new circumstances.

Based on the results discussed in this paper, the current study contributes to the goal

147

orientation literature by providing some insight into the processes involved in the

dynamic nature of state goal orientations.

148

Appendix A
Sample Trial
Number of Outstanding Shares: 500,000
Previous Earnings (Last Year): $1,500,400
Projected Earnings (Current Year): $2,000,000
Total Assets: $9,500,000
Total Liabilities: $750,000

What is the closing stock price (per share)?

149

Appendix B
Pilot Study
Pumse of Study

One of the primary issues of interest in the main study was subjects’ reactions to
the reception of positive vs. negative performance feedback. As such, it was imperative
that relatively equal numbers of individuals received positive and negative performance
feedback. In order to provide subjects with normative performance feedback that was
accurate and believable, a norm had to be established.

M99135!

Subjects. Forty-nine undergraduate students participated as subjects in the pilot
study in exchange for research participation points to be applied toward their introductory
psychology class. Seven subjects were removed from analyses because they did not
ﬁnish the experiment. Of the 42 subjects in the ﬁnal sample, 56% were female, 34%
were freshmen, and their mean age was 19.7 years (S__D = 1.46). Thus, these subjects
were representative of the subjects employed in the main study.

Procedure. After subjects provided their written consent to participate in the
experiment (see Appendix K), they were asked to respond to the demographics survey.
Upon completion of the survey, subjects read some instructions on their computer
screens. These instructions included a general overview of the task as well as necessary
information for performing the MCPL task. Subjects were then asked to complete three
trial blocks (i.e., 45 trials). Subsequent to each trial, individuals were provided with the
accuracy feedback (i.e., direction and magnitude) as described in the main study;

however, positive vs. negative normative performance feedback was not provided. Upon

150

completion of the study, subjects were debriefed completely (see Appendix L) and
thanked for their participation.
ﬂew—Its

Because normative data would be provided to subjects in the main study in the
form of percentiles (e.g., “You scored at the 86‘h percentile”), it was imperative that the
particular norms employed for each block came from normal distributions of error scores.
To achieve this goal, outliers (i.e., individuals who scored at least three standard
deviations from the mean) were eliminated on a block-by-block basis. This method
resulted in a total of 35 subjects in Block 1, 40 subjects in Block 2, and 39 subjects in
Block 3. Resulting univariate statistics for each distribution of error scores are displayed
in the table below. The means and standard deviations were used for the respective

norms in the main study.

 

Block n Mean St. Dev. Median Skewness Kurtosis
1 35 20.76 9.95 16.93 1.36 1.02
2 40 17.42 4.18 17.23 .32 .31

3 39 10.90 3.52 9.77 1.47 2.84

151

Appendix C

State Goal Orientations
(Adapted from VandeWalle, 1997)

INSTRUCTIONS: Please read the following statements carefully, and indicate the
extent to which you agree or disagree with each statement using the scale provided.
There are no right or wrong answers to these questions.

9’1"!”

PM“

7.

8.

9.

1 = strongly disagree

2 = disagree
3 = neutral
4 = agree

5 = strongly agree

I’m concerned about letting others realize that I have low ability on this task. (A)
I’m concerned about showing that I can perform better than others on this task. (P)

I think that this task has the opportunity for me to develop new skills and knowledge.
(M)

My goal on this task is to avoid doing poorly. (A)

On this task, I am more concerned with avoiding a show of low ability than learning a
new skill. (A)

I enjoy engaging in this task because I think it requires a high level of ability and
talent. (M)

On this task, I am more concerned with proving my ability than with learning a new
skill. (P)

My goal on this task is to develop my skills. (M)

On this task, the development of my ability rs important enough to take risks. (M)

10. On this task, I try to ﬁgure out what it takes to prove my ability to others. (P)
11. I am concerned with hiding the fact that I do not know what I am doing on this task.

(A)

12. My goal on this task is to perform well. (P)

Note: M = mastery; P = prove; A = avoid

152

Appendix D

Trait Goal Orientations
(VandeWalle, 1997)

INSTRUCTIONS: Please read the following statements carefully, and indicate the
extent to which you agree or disagree with each statement using the scale provided.

There are no right or wrong answers to these questions.

1 = strongly disagree

2 = disagree
3 = neutral
4 = agree

5 = strongly agree

1. I’m concerned about taking on a task at school if my performance would reveal that I

had low ability.

I’m concerned with showing that I can perform better than my peers. (P)

I am willing to select a challenging assignment that I can learn a lot from. (M)

I often look for opportunities to develop new skills and knowledge. (M)

Avoiding a show of low ability is more important to me than learning a new skill. (A)

I prefer to avoid situations at school where I might perform poorly. (A)

I prefer to work engage in situations that require a high level of ability and talent. (M)

For me, development of my ability is important enough to take risks. (M)

I try to ﬁgure out what it takes to prove my ability to others. (P)

10. I enjoy challenging and difﬁcult tasks where I’ 11 learn new skills. (M)

11. I enjoy it when others are aware of how well I am doing. (P)

12. I prefer to work on projects where I can prove my ability to others. (P)

13. I would avoid taking on a new task if there was a chance that I would appear rather
incompetent to others. (A)

owseweww

Note: M = mastery; P = prove; A = avoid

153

Appendix E

Self-Efﬁcacy
(Kozlowski et al., in press)

INSTRUCTIONS: Please read the following statements carefully, and indicate the
extent to which you agree or disagree with each statement using the scale provided.
There are no right or wrong answers to these questions.

1 = strongly disagree

2 = disagree
3 = neutral
4 = agree

5 = strongly agree

I can meet the challenges of this task.
I am conﬁdent in my understanding of how information cues are related to decisions.
I can deal with decisions under ambiguous conditions.

I am certain that I can manage the requirements of this task.

I believe I will fare well in this task if the workload is increased.

1 am conﬁdent that I can cope with this task if it becomes more complex.
I believe I can develop methods to handle changing aspects of this task.

I am certain I can cope with task components competing for my time.

P°>'.°‘S":“P’!°f"

154

Appendix F

Openness to Experiences
(Costa & McRae, 1992)

INSTRUCTIONS: Please read the following statements carefully, and indicate the
extent to which you agree or disagree with each statement using the scale provided.

There are no right or wrong answers to these questions.

1 = strongly disagree

2 = disagree
3 = neutral
4 = agree

5 = strongly agree

I don't like to waste my time daydreaming. (-)

Once I ﬁnd the right way to do something, I stick to it. (-)

I am intrigued by the patterns I ﬁnd in art and nature.

I believe letting students hear controversial speakers can only confuse and mislead

them. (-)

Poetry has little or no effect on me. (-)

I often try new and foreign foods.

I seldom notice the moods or feelings that different environments produce. (-)

I believe we should look to our religious authorities for decisions on moral issues. (-)

Sometimes when I am reading poetry or looking at a work of art, I feel a chill or wave

of excitement.

10. I have little interest in speculating on the nature of the universe or the human
condition. (-)

11. I have a lot of intellectual curiosity.

12. I often enjoy playing with theories or abstract ideas.

99°F!"

99°99.“

Note: - = reverse keyed item

155

 

Appendix G

Agreeableness
(Costa & McRae, 1992)

INSTRUCTIONS: Please read the following statements carefully, and indicate the
extent to which you agree or disagree with each statement using the scale provided.

There are no right or wrong answers to these questions.

1 = strongly disagree

2 = disagree
3 = neutral
4 = agree

5 = strongly agree

I try to be courteous to everyone I meet.

I often get into arguments with my family and co-workers. (-)

Some people think I'm selﬁsh and egotistical. (-)

I would rather cooperate with others than compete with them.

I tend to be cynical and skeptical of others' intentions. (-)

I believe that most people will take advantage of you if you let them. (-)
Most people I know like me.

Some people think of me as cold and calculating. (- )

I'm hard- headed and tough-minded in my attitudes. (-)

10.1 generally try to be thoughtful and considerate.

11. If] don't like people, I let them know it. (-)

12. If necessary, I am willing to manipulate people to get what I want. (-)

“@HQMPPN!‘

Note: - = reverse keyed item

156

 

Appendix H

Self-Monitoring
(Snyder, 1974)

INSTRUCTIONS: Please read the following statements carefully, and indicate the
extent to which you agree or disagree with each statement using the scale provided.

There are no right or wrong answers to these questions.

1 = strongly disagree

2 = disagree
3 = neutral
4 = agree

5 = strongly agree

1. I ﬁnd it hard to imitate the behavior of other people. (-)

2. My behavior is usually an expression of my true inner feelings, attitudes, and beliefs.
(-)

3. At parties and social gatherings, I do not attempt to do or say things that others will

like. (-)

I can only argue for the ideas which I already believe. (-)

I can make impromptu speeches even on topics about which I have almost no

information.

6. I guess I put on a show to impress or entertain people.

7. When I am uncertain how to act in a social situation, I look to the behavior of others
for cues.

8. I would probably make a good actor.

9. I rarely need the advice of my friends to choose movies, books, or music. (- )

10.1 sometimes appear to others to be experiencing deeper emotions than I actually am.

11. I laugh more when I watch a comedy with others than when alone.

12. In a group of people I am rarely the center of attention. (-)

13. In different situations and with different people, I often act like a very different
person.

14. I am not particularly good at making other people like me. (-)

15. Even if I am not enjoying myself, I often pretend to be having a good time.

16. I’m not always the person I appear to be.

17. I would not change my opinions (or the way I do things) in order to please someone
else of win their favor. (-)

18. I have considered being an entertainer.

19. In order to get along and be liked, I tend to be what people expect me to be rather
than anything else.

20. I have never been good at games like charades or improvisational acting. (-)

21. I have trouble changing my behavior to suit different people and different situations.
(-)

22. At a party I let others keep the jokes and stories going. (-)

23. I feel a bit awkward in company and do not show up quite so well as I should. (-)

PM“

157

24. I can look anyone in the eye and tell a lie with a straight face (if for a right end).
25. I may deceive people by being friendly when I really dislike them.

Note: - = reverse keyed item

158

 

Appendix 1
Performance Evaluations
INSTRUCTIONS: Please read the following statements carefully, and indicate the
extent to which you agree or disagree with each statement using the scale provided.

There are no right or wrong answers to these questions.

1 = strongly disagree

2 = disagree
3 = neutral
4 = agree

5 = strongly agree

I think my performance on this task was a success.
I do not think I performed well on this task. (-)

I think I succeeded at this task.

I think my performance on this task was poor. (-)
I think I performed well on this task.

P'PP’NI"

Note: - = reverse keyed item

159

Appendix J
Affect (Elation vs. Depression; Relief vs. Anxiety)
INSTRUCTIONS: Please read the following statements carefully, and indicate the
extent to which you agree or disagree with each statement using the scale provided.

There are no right or wrong answers to these questions.

1 = strongly disagree

2 = disagree
3 = neutral
4 = agree

5 = strongly agree

My performance on this task makes me sad. (J -)

I am excited about my performance on this task. (J)

Thinking about my performance on task depresses me. (J -)

My performance on this task makes me happy. (J)

I am happy when I think about my performance on this task. (J)

I am relieved when I think about my performance on this task. (D)
My performance on this task agitates me. (D -)

Thinking about my perforrnance on this task makes me anxious. (D -)
I am nervous when I think about my performance on this task. (D -)
0. My performance on this task lifts a burden off of my chest. (D)

“PPSP‘MPP’PE‘

Note: J = joy (elation vs. depression); D = distress (relief vs. anxiety); - = reverse keyed

item

160

 

Appendix K
Feedback Believability

INSTRUCTIONS: Please read the following statements carefully, and indicate the
extent to which you agree or disagree with each statement using the scale provided.
These items refer to the feedback that you received indicating whether you achieved your
goal and how you did compared to other people. There are no right or wrong answers to
these questions.

= strongly disagree
2 = disagree
3 = neutral
4 = agree

5 = strongly agree

I think that the feedback was accurate.

I think that the feedback was an overestimate of my performance. (-)
I think that the feedback was believable.

I think that the feedback was inaccurate. (-)

I think that the feedback was an underestimate of my performance. (-)

MPP’NE‘

Note: - = reverse keyed item

161

 

Appendix L
Demographics

INSTRUCTIONS: Please read each of the following questions carefully and provide the
most accurate answer that you can.

1. What is your sex?
a) Male
b) Female
2. What is your age?
What is your year in school?
a) Freshman
b) Sophomore
c) Junior
(1) Senior
4. What is your major?
a) Business
b) Communications
c) Education
(1) Engineering
e) Mathematics
f) Psychology
g) Sociology
h) Other
5. What is your overall GPA? (Indicate “No GPA” if you do not have a GPA yet)
a) 0.0-0.5
b) 06-10
C) 1.1-l.5
d) 1.6-2.0
e) 2.1-2.5
f) 2.6-3.0
g) 3.1-3.5
h) 3.6-4.0
i) No GPA
6. What was your ACT or SAT score? (If you took both tests, please report your SAT
score.)
7. What are your plans after graduation?
a) Graduate school
b) Work
c) Don’t know

9°

162

Appendix M

Informed Consent

Subject’s Name: Date:

 

Project Title: Can You Make It On Wall Street?
Investigators' Names: Brad Chambers and Dr. Richard DeShon

Description and This study investigates how individuals use multiple

Explanation of Procedure: sources of information when making decisions. In this
experiment, you will be asked to predict stock market
prices based on a variety of factors as well as answer
several questions.

Estimated time required: 2 hours (4 half-hour credits)

Risks and discomforts: None

Thank you for participating in our study! If you have any questions or concerns about
this study, please feel free to contact either Brad Chambers at 355-2171 or Professor
Richard DeShon at 353-4626. If, at any time, you feel your questions have not been
adequately answered, you may speak with the Head of the Department of Psychology
(Dr. Neal Schmitt, 355-9563), or the University Committee on Research Involving
Human Subjects (Dr. David E. Wright, 355-2180). Your participation in this research is
voluntary. You are free to withdraw this consent and discontinue participation in this
project at any time without penalty. If you choose to withdraw from the study prior to its
completion, you will receive credit only for the time you have spent in the study. You
can be removed from the study for disruptive behavior. If you are removed from the
study, you will not receive credit for your participation. Within one year of your
participation, a copy of this consent form will be provided to you upon request.

I freely give permission for my participation in this study.

 

Signature

163

Appendix N
Task Instructions

In this study, you will be asked to assume the role of a stockbroker. Basically, a
stockbroker is someone who buys and sells stocks on the stock market for other people--a
stockbroker is hired by an investor to take care of all the technical stuff.

Often times, stockbrokers are asked by their clients (the investors) to predict the
closing price of certain stocks. The closing price of a stock is the amount of money that
the stock is worth at the end of the day. When stockbrokers are asked to predict the
closing price of stocks, they take many things into consideration. Speciﬁcally, the
following factors are important when predicting the closing prices of stocks:

1) Number of Outstanding Shares: The total number of shares ('pieces' of a stock)
that are owned by stock holders.

2) Previous Earnings (for last year): The total amount of money that a company
earned last year.

3) Projected Earnings (for the current year): The total amount of money that a
company estimates that it will earn in the current year.

4) Total Assets: The total value (in dollars) of a company's assets, including
property, inventory, proﬁts, etc. i

5) Total Liabilities: The total value (in dollars) of a company's liabilities (what it

owes), including building rent, wages, utility costs, loans, etc.

It is very important that you ignore any information that you may already know
about the stock market and how these various factors inﬂuence stock prices.

As you may have already guessed, it is very important for a stockbroker to be able
to make accurate predictions. If he/she overestimates the closing price of a stock, people
may hold onto their stocks for too long because they think it will be worth more later--in

this case, they would lose money because they should have sold the bad stock and bought

164

a better one. If the stockbroker underestimates the closing price of a stock, people may
sell the stock too early-~in this case, they would also lose money because they sold the
stock too soon. In either case, the investor would lose money because the stockbroker's
prediction was not accurate.

In this study, you will be asked to predict the closing prices of some stocks based
on the ﬁve factors that I described earlier. For example, you will be presented with the
following information, and you will be asked to predict the stock price.

The following is the information for the XYZ company.
Number Outstanding Shares: $ 500,000

Previous Earnings (last year): $ 1,500,400

Projected Earnings (current year): $ 2,000,000

Total Assets: $ 9,500,000

Total Liabilities: $ 750,000

You will have to ﬁgure out how all of the different factors and information go
together to come up with the stock prices. Again, it is very important that you ignore
anything that you may already know about the stock market and how stock prices are
created (except for the information that I have given- to you).

You will be able to use some scrap paper and the calculator on your computer. To
switch between the experiment and the calculator, hold down the ALT key then press the
TAB key at the same time. Please be careful not to close either window, just switch
between the two. Does everyone understand what I am talking about?

Throughout this study, you will receive two kinds of feedback. First, after each
stock price that you predict, you will be told how accurate your predictions are compared

to the actual stock prices. Second, at various times throughout the study, you will be

165

given feedback indicating how accurate your predictions are compared to the predictions
of other people.

As you have probably learned in your psychology classes, the feedback that
researchers provide to participants regarding their performance is often inaccurate or
deceptive. That is, researchers often "lie" to their participants as part of their studies.
The study that you are participating in today is not like these other studies. Instead, the
feedback that you are given today will be 100% truthful. It is very important that you
realize this.

In addition to predicting the stock prices, you will be asked to answer several
questions at various points throughout the study. In doing so, you will answer some of
the same questions more than once. Even though it may seem tedious, these questions

are very important, so please answer each item carefully and honestly each time.

ONE OF THE THREE GOAL ORIENTATION MANIPUIATIONS

MASTERY: Successful performance on this task requires the use of skills that
must be developed and practiced. At ﬁrst, you should expect to make a lot of mistakes,
but this is ok because you can learn from your mistakes. Through practice, though, you
will be able to develop the necessary skills to increase your performance. It is important
for you to remember that the more practice you have, the better you will be able to
perform on this task. Thus, you should focus your attention on developing your skills

and mastering the task rather than on the mistakes that you make.

166

PROVE: Successful performance on this task is based on skills and abilities that
either you do or do not already have. We are interested in comparing your performance
on this task to that of other individuals so that we can examine different individuals'
underlying abilities to perform this task. Some students really stand out because they do
so much better than everyone else; if you perform better than most other students do, this
would indicate that you have more ability than they do. So you should really focus on

demonstrating how good you are. Remember, you already have the ability.

AVOID: Successful performance on this task is based on skills and abilities that
either you do or do not already have. We are interested in comparing your performance
on this task to that of other individuals so that we can examine different individuals'
underlying abilities to perform this task. Some students really stand out because they do
so much worse than everyone else; if you perform worse than most other students do, this
would indicate that you have less ability than they do. So you should avoid making
mistakes that will make you appear incompetent. Remember, you already have the

ability.

Now that I have described to you what you will be doing today, I would like for you to

answer a few more questions.

167

Appendix 0
Debrief

The experiment you just completed was concerned with how individuals’ motivation
changes across time and after receiving feedback about their performance. The problems
that you were asked to solve were designed so that they would be impossible for you to
solve. The algorithm used to determine stock prices in this experiment was nearly
impossible to guess. Thus, because the experiment was designed in this manner, you had
very little chance (if any) of success. It is very important that you realize that your
performance in this experiment was not an accurate depiction of your actual abilities.

Please do not discuss this experiment with anyone else, for it is important that future
subjects know nothing about the experiment before they begin.

The data you provided today is important to us, and we appreciate your help. If you have

any questions or comments about today's experiment, please talk to the experimenter now
or contact Brad Chambers, Department of Psychology, 22 Baker Hall, (517) 355-2171.

168

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