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6. Intrinsic motivation and creativity: Opening up a
black box
Logan M. Steele, Tristan McIntosh, and Cory Higgs
What motives impel the creative act? Is the creative act merely a means in the service of other
ends, or is it sought as an end in itself? How does the nature of the motivation for a given crea-
tive act affect the likelihood of its achievement?
(Crutchfield, 1962, p. 121)
Much of the research on motivation and creativity has revolved around intrinsic motiva-
tion, which has been presumed to play a vital role in motivating creative performance.
Indeed, it is generally thought that intrinsic motivation is the central mediating mecha-
nism through which personal and contextual factors influence creativity (Amabile, 1996;
Shalley, Zhou, & Oldham, 2004). From where did this idea emerge? And what evidence is
there to support it? In this chapter, we examine the literature bearing on the relationship
between intrinsic motivation and creativity. We then explore how this foundational mech-
anism has influenced the ways in which contextual factors are thought to impact creativ-
ity. From here, we work to unpack this “black box” of motivating creativity by examining
the ways in which intrinsic motivation relates to other self-regulatory processes involved
in creative performance (e.g., self-efficacy), to creative problem-solving processes, and
ultimately to creative outcomes.
Defined as the production of novel and useful ideas (Barron, 1955; Runco & Jaeger,
2012), creativity has long thought to be preceded by profound interest in and engagement
with a task. Arthur Schawlow, Nobel Prize-winning physicist said: “The labor of love
aspect is important. The most successful scientists often are not the most talented. But
they are the ones who are impelled by curiosity. They’ve got to know what the answer
is” (Schawlow, 1982, p. 42). Building off a host of anecdotal evidence, such as this, and
numerous case studies, Amabile (1983a, 1983b, 1988) formally introduced in a theoretical
model the central role of intrinsic motivation in the creative process. Indeed, according
to the “intrinsic motivation principle of creativity” (Amabile, 1997), without this com-
ponent, no “recognizable level of creativity is to be produced” (Amabile, 1983b, p. 367).
Prior to Amabile’s social psychological model of creativity (1983a, 1996), external
influences on the creative process were largely ignored. Seemingly, the presumption was
that the “creative person” could do nothing but produce creative work. Of course, this
is notthe case. Recognizing the potent influence of external variables, Amabile (1996)
expanded the theory of creativity, which is generally held to consist of four processes
(Amabile, 1996; Guilford, 1950; Lubart, 2001; Montag, Maertz, & Baer, 2012; Mumford,
Mobley, Uhlman, Reiter-Palmon, & Doares, 1991; Wallas, 1926): problem definition,
information gathering, idea generation, and idea evaluation. Amabile’s (1983b) major
contribution of to extant theory was the incorporation of and centrality assigned to a
motivational component in a cognitive processing framework. By doing so, Amabile
(1983b) also introduced the idea that creativity could be disrupted to the extent that
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Intrinsic motivation and creativity 101
intrinsic motivation was diminished. In the following section, we will describe in greater
detail Amabile’s (1996) model of creativity and the role intrinsic motivation is expected
to play in the creative process. We then examine the research on intrinsic motivation more
broadly, the long-standing debate over the undermining effect of extrinsic motivation,
and the way in which this debate has informed creativity research over the past 30 years.
Next, we present an alternative conception of motivating creative performance based on
self-regulation theory. This chapter concludes by discussing the implications of this new
perspective for leaders of creative efforts.
AMABILE’S COMPONENTIAL MODEL OF CREATIVITY
As mentioned above, Amabile’s (1996) model of creativity builds on the four-step con-
ceptualization posited by earlier theorists (Guilford, 1950; Wallas, 1926). To this four-step
process she added three components that are thought to interact with the creative process
in different ways and at different stages. These three components include domain-relevant
knowledge and skills, creativity-relevant knowledge and skills, and task motivation.
Domain-relevant Knowledge and Skills
The criticality of expertise in creativity has been well established across an array of
domains (Baer, 2015). Relevant knowledge and skills form the foundation from which
creativity can emerge. Wallas (1926), one of the earliest creativity theorists, suggested that
the first step in the creative process is being knowledgeable about the domain at hand. A
new idea cannot come from nowhere; a new idea is based on available information that
has been combined with other information and reorganized (Baughman & Mumford,
1995; Mobley, Doares, & Mumford, 1992). Thus, the more knowledge a person has and
the better one understands the relationships between pieces of information, the greater
the likelihood of coming up with a creative idea. The depth of understanding informa-
tion is a critical marker of expertise (Ericsson & Charness, 1994). With expertise come a
number of benefits to creativity. For instance, experts are better able to extract abstract
principles for solving a problem rather than having to rely on routine approaches. Use of
these abstract principles has been found to facilitate creative problem solving (Wickelgren,
1979).
Creativity-relevant Knowledge and Skills
This component of Amabile’s (1996) model is referred to as the “something extra of crea-
tive performance” (Amabile, 1983b, p. 364). It is what differentiates a useful and appropri-
ate idea from a truly creative one. The difference here is in the novelty or originality of
the idea, which Amabile (1996) theorizes to emerge from three areas of creativity-relevant
knowledge and skills. The first area is a cognitive style. Broadly speaking, the ways in
which this cognitive style is held to impact creativity is through understanding complexity
and being able to “break set” or think about problems in new ways (Amabile, 1996, p. 88).
This cognitive style involves a set of nine of skills (Amabile, 1996): (1) breaking percep-
tual set, (2) breaking cognitive set, (3) understanding complexities, (4) keeping response
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102 Handbook of research on leadership and creativity
options open as long as possible, (5) suspending judgment, (6) using “wide” categories,
(7) remembering accurately, (8) breaking out of performance routines, and (9) perceiving
creatively.
The second area of creativity-relevant knowledge and skills is a knowledge of heuristics
for generating novel ideas. These heuristics are rules of thumb that can aid in breaking
from established ways of approaching or solving a problem. For example, “When all else
fails, try something counterintuitive” (Newell, Shaw, & Simon, 1962), “Make the familiar
strange” (Gordon, 1961, p. 33), or play with ideas by engaging in “mental gymnastics”
(Wickelgren, 1979).
The third area of creativity-relevant knowledge and skills is work style, of which
there are four elements (Amabile, 1996): (1) being able to focus for long periods of time
(Campbell, 1960; Hogarth, 1980; Prentky, 1980), (2) being able to set aside unproductive
strategies and temporarily put off problems on which progress is not being made (Simon,
1966), (3) persisting through difficulty (Roe, 1953; Walberg, 1971), and (4) having a will-
ingness to work hard and expend a great deal of effort (Bergman, 1979; Bloom, 1956;
Davis & Rimm, 1977; Simonton, 1980; Wallach & Kogan, 1965).
Task Motivation
Broadly speaking, motivation refers to a set of internal forces that underlie the direction,
intensity, and persistence of behavior or thought (Pinder, 2008). Since very early in crea-
tivity research, it has been thought that engagement in a task for the sake of the task itself
is crucial for creativity (Crutchfield, 1962; Osborn, 1963; Rogers, 1954). Consequently,
to the extent that task-involved motivation is undermined, it was also thought that
motivation that emerges from external pressure or control is detrimental to creativity
(Crutchfield, 1962). Building on this research, Amabile (1983a) proposed that there are
two elements of task motivation: (1) baseline attitude toward the task, and (2) percep-
tion of the reasons for undertaking the task. The first element is formed based on an
appraisal of how closely the task matches one’s interests. The second element, in contrast,
is largely based on social and environmental factors. As a combination of these elements,
task motivation varies from the baseline level according to the level of perceived reasons
for working on a task. To the extent that a person’s reasons for working on a task are
viewed as controlling or irrelevant to the task, one’s baseline level of internal motivation
decreases. This framework of task motivation is based on a social psychological view of
intrinsic motivation (DeCharms, 1968; Deci, 1975; Greene & Lepper, 1978), which posits
that one’s motivation to engage in a task as an end in itself can be affected by external
factors. In line with the “overjustification hypothesis” (DeCharms, 1968; Kelley, 1973),
it is thought that extrinsic constraints will undermine intrinsic motivation and, conse-
quently, impair creative performance.
Amabile (1996) has since revised this original conception of task motivation. First,
the definitions of intrinsic and extrinsic motivation were modified. Intrinsic motiva-
tion arises from a person’s positive reactions to the qualities of the task, while extrinsic
motivation arises from any sources outside of the task itself. The second revision was
done with respect to the mechanisms through which intrinsic and extrinsic motivation
combine and interact. Originally, extrinsic motivation was viewed as being detrimental
to creativity because as it would increase, intrinsic motivation would decrease (Calder &
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Intrinsic motivation and creativity 103
Staw, 1975). However, over the course of several studies, Amabile and colleagues observed
that extrinsic motivation was not always inversely related to intrinsic motivation, and, in
fact, they may operate additively (e.g., Amabile, Conti, Coon, Lazenby, & Herron, 1996;
Amabile, Hennessey, & Grossman, 1986). In 1993, Amabile proposed a theory of motiva-
tional synergy. Central to this conceptualization was differentiating two types of extrinsic
motivation – informational (e.g., constructive feedback) and controlling (e.g., deadlines,
constraints) (Deci & Ryan, 1985). Informational extrinsic motivation, rather than under-
mining intrinsic motivation, is thought to operate “synergistically” with intrinsic motiva-
tion by confirming one’s sense of competence or facilitating deeper involvement in the
task (Amabile, 1993). In contrast, controlling extrinsic motivation is thought to operate
“non-synergistically” with intrinsic motivation by undermining one’s sense of autonomy
and disrupting engagement with the task (Amabile, 1993).
Processes in the Componential Model
Each of the three components reviewed above are theorized to play different roles at each
stage of the creative process (Amabile, 1996). Task motivation is responsible for the initia-
tion and maintenance of creativity. To begin the creative process, it is believed to be neces-
sary for an individual to have a high level of intrinsic motivation. Sometimes, the problem
must be discovered; at other times, the problem is presented (Unsworth, 2001). It is less
likely that a person will be as intrinsically motivated to solve a presented problem as one
would be to solve a discovered problem, due to the improbability that a presented problem
will match one’s interests (Amabile, 1996). In the next stage of the creative process, infor-
mation gathering, task motivation is not theorized to play an important role. Instead,
domain-relevant knowledge and skills are thought to be critical. To the extent that one’s
knowledge and skills are well developed, less time is needed at this stage. In the third stage
of the creative process, idea generation, task motivation again plays an important role,
as do creativity-relevant knowledge and skills. Creativity-relevant knowledge and skills
contribute to the flexibility with which cognitive pathways and strategies are explored,
the attention given to certain pieces of information, and the degree to which a particular
pathway is pursued. Task motivation can enhance the processes executed in this phase
by facilitating a willingness to take risks and to incorporate information that may not be
seen as directly relevant. More information, together with effective strategies for exploring
this information, results in an increasing likelihood of producing something that is novel
and useful. Finally, in the fourth stage, where the generated ideas are evaluated, domain-
relevant knowledge and skills again play an important role. Domain-relevant knowledge
and skills are used to inform the standards against which the appropriateness of ideas
are evaluated.
When a product is a perfect success or complete failure, the creative process stops. A
person either proceeds to a new problem or to the next relevant facet of the problem
at hand. The process repeats, however, as long as there is a sense of progress (Simon,
1978), beginning with a reconsideration of how the problem was defined. Critically, task
motivation must remain high enough to re-engage the problem. After success, intrinsic
motivation for other tasks in the same domain is expected to increase. On the other hand,
complete failure is expected to reduce intrinsic motivation. When the result is somewhere
in between, and a person has a sense of progress, one’s level of intrinsic motivation is
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104 Handbook of research on leadership and creativity
expected to increase because the solver is approaching the goal of finding a viable solution
(Huang, Zhang, & Broniarczyk, 2012). Once the solver no longer perceives that progress
is being made, intrinsic motivation will decrease (Johnson, Howe, & Chang, 2013). This
relationship between progress and motivation is based on Harter’s (1978) and White’s
(1959) research on effectance motivation and the urge toward competence. Experiencing
success leads to feelings of satisfaction, an increased sense of efficacy, and higher levels of
intrinsic motivation. These motivational forces then produce more attempts at mastering
the task at hand. Furthermore, these attempts at mastery contribute to acquiring domain-
and creativity-relevant knowledge and skills, which make future success more probable.
In sum, intrinsic motivation is thought to promote creativity through the rewarding
feelings of competence and interest, which contribute to the continued development
of domain- and creativity-relevant knowledge and skills. As effective creative problem-
solving strategies become increasingly routine, the creative products that result from these
processes are likely to become more frequent. Thus, intrinsic motivation appears critical
for creative performance, and while this idea has been around for centuries, it was formally
theorized in Amabile’s (1983a, 1996) social psychological model of creativity. It has since
been a focal variable in creativity research and, as a result, has had a significant impact
on what constitutes effective leadership of creative efforts. Specifically, the long-standing
debate on the undermining effect of extrinsic motivation on intrinsic motivation has led
to the conclusion that creativity will increase to the extent that the latter is enhanced and
the former diminished. From a leadership perspective, the most effective means of accom-
plishing this is providing autonomy, due to the central role of perceptions of autonomy
in intrinsic motivation (Gagné & Deci, 2005). In the following section, this assertion will
be explored more fully, beginning with a review of the construct of intrinsic motivation,
followed by a summary of the debate over the undermining effect of extrinsic motivation,
and the implications of this debate on the link between leaders and creativity.
INTRINSIC MOTIVATION
History of Intrinsic Motivation
The study of intrinsic motivation emerged in a time where behaviorism dominated the
psychological landscape (Skinner, 1950). Defined as doing a task for its own sake, out
of interest or enjoyment (Deci, 1971), intrinsic motivation departed from drive theories
of motivation, which emphasize reducing anxiety and maintaining homeostasis as the
forces that compel behavior (Hull, 1943). Instead, intrinsic motivation seems to energize
exploratory behaviors, which are often accompanied by excitement rather than anxiety
reduction (Harlow, 1953). White (1959) originally referred to the motivation to explore
as effectance motivation. He suggested that the basis of effectance motivation is a need
for competence, which was defined as the “capacity to interact effectively with [the]
environment” (White, 1959, p. 297). Nearly ten years later, DeCharms (1968) expanded
on this idea by proposing that intrinsically motivated behavior resulted also from a need
to feel personal causation.
Deci (1971) combined White’s (1959) notion of effectance motivation with DeCharms’s
(1968) need for personal causation to create the first experiment on intrinsic motiva-
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tion with human participants. In this experiment, Deci (1971) investigated the effects of
receiving external rewards on intrinsic motivation. Over the course of three one-hour
sessions, participants were asked to solve four puzzles. The only difference between the
experimental and control conditions was that during the second session participants in the
experimental condition were offered one dollar for every puzzle they completed within a
specified time limit. In the third session, Deci (1971) observed that the control group spent
more time on average working on the puzzles relative to the first and second sessions. In
contrast, while the experimental group spent 26 percent more time on the puzzles when the
reward was present, they spent 20 percent less time, relative to their first session, when the
reward was taken away. However, there were no significant differences between conditions
or sessions in terms of level of interest and enjoyment.
Following this and two other experiments, Deci (1971) suggested that the reason salient,
external rewards undermine intrinsic motivation is that they prompt a change in where
one perceives the locus of causality of behavior from internal to external (DeCharms,
1968). In other words, external rewards lead people to see their behavior as emerging from
coercion or another’s control rather than from one’s sense of self. This perspective came to
be known as cognitive evaluation theory (Deci, 1975). Central to this theory is the assump-
tion that people need to feel autonomous, so external events that enhance one’s perception
of autonomy (e.g., leaders providing subordinates with a choice in what task to complete
or how to complete tasks) will, in turn, facilitate intrinsic motivation. By implication,
events that decrease one’s perception of autonomy will undermine intrinsic motivation.
This proposition has generated a long and spirited debate, which we review next.
Debate Over the Undermining Effect
The debate over the undermining effect of external rewards on intrinsic motivation is
one of the most heated in the applied psychology literature. Nine meta-analyses have
been conducted investigating this issue (Cameron, Banko, & Pierce, 2001; Cameron &
Pierce, 1994; Deci, Koestner, & Ryan, 1999a, 1999b, 2001; Eisenberger & Cameron, 1996;
Eisenberger, Pierce, & Cameron, 1999; Rummel & Feinberg, 1988; Tang & Hall, 1995;
Wiersma, 1992). After Deci’s (1971) original article on the undermining effect, dozens
of studies were conducted that provided more and more evidence of controlling exter-
nal events (e.g., threat of punishment, deadlines, evaluation, competition, surveillance)
diminishing intrinsic motivation (Deci & Ryan, 1985). Furthermore, other studies showed
that controlling external events influence the type of activities people will choose. For
instance, after receiving a reward for completing an experimental task, second-grade stu-
dents subsequently preferred working on simpler versions of the task during a free-choice
period (Pittman, Emery, & Boggiano, 1982). This is in contrast to the unrewarded and
non-contingently-rewarded students who preferred more complex versions of the task
during the free-choice period (Pittman et al., 1982).
Debate over the undermining effect seemed to come to head in an exchange published
in Psychological Bulletin in 1999 (Deci et al., 1999a, 1999b; Eisenberger et al., 1999;
Lepper, Henderlong, & Gingras, 1999). After providing a summary and critique of previ-
ous meta-analyses, Deci and colleagues (1999a) provided a meta-analysis of their own.
They observed that tangible rewards (e.g., money, marshmallows) had a significant nega-
tive effect on intrinsic motivation for interesting tasks. The one exception to this finding
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106 Handbook of research on leadership and creativity
was that while performance-contingent rewards were negatively related to free-choice
behavior, they were unrelated to self-reported interest. In contrast to tangible rewards,
verbal rewards (i.e., positive feedback) were positively related to intrinsic motivation.
Eisenberger and colleagues (1999) responded with another meta-analytic study. Unique
to this study was the categorization system for performance-contingent reward studies.
Instead of being grouped into a single category, three new categories were developed:
rewards for outperforming others, rewards for meeting absolute performance stand-
ards, and rewards for meeting vague or unspecified performance standards (e.g., “doing
well”). In this meta-analysis, performance-contingent rewards had a positive, but non-
significant, effect on free-choice behavior. After treating the clarity of performance
standards as a moderator, it was revealed that with vague performance standards, per-
formance-contingent rewards significantly and negatively affect free-choice behavior; the
directionality flips, however, when the performance standards are explicit. With respect
to self-reported interest, performance-contingent rewards had a significant positive effect,
which is in contrast to the non-significant effect observed by Deci et al. (1999a). Again, the
clarity of performance standards was found to be an important moderator. With vague
performance standards, no effect was observed, but with explicit standards, the effect was
positive and significant.
Deci and colleagues (1999b) replied with a number of criticisms of Eisenberger et al.’s
(1999) meta-analysis. Deci et al. (1999b) argue that that the primary reason for the differ-
ences between their respective meta-analyses is that Eisenberger et al. (1999) excluded over
25 percent of the studies included in Deci et al. (1999b). Deci et al. (1999b) considered the
rationale provided by Eisenberger et al. (1999) untenable. Due to this and other objections,
Deci et al. (1999b) concluded that the only reliable, new finding reported by Eisenberger et
al. (1999) was that performance-contingent rewards can foster intrinsic motivation when
people are given high, competitive performance standards (e.g., outperform 85 percent
of other participants). This exchange appeared to settle the debate – a few exceptions
notwithstanding, tangible rewards undermine intrinsic motivation.
However, in the last decade-and-a-half, the view on the undermining effect has changed
considerably. An issue of central importance is the lack of generalizability of the under-
mining effect. Deci et al.’s (1999a) meta-analysis, for example, did not include a single
study of work-related behavior. In all of the 128 studies examined, the focal outcomes
were what people did during free time and their level of self-reported interest. There are
important differences between job performance and free-choice behavior, something that
has been highlighted by other authors (e.g., Wiersma, 1992). Furthermore, “On a funda-
mental level, the debate fails to recognize that performance is not simply determined by
one or the other: To some degree both intrinsic and extrinsic motivation are functional
in performance contexts” (Cerasoli, Nicklin, & Ford, 2014, pp. 981–982, emphasis in
original). Thus, practically speaking, it is irrelevant how people choose to spend time when
they are not being rewarded or compensated (Bartol & Locke, 2000). In the workplace,
rewards (e.g., pay) are an unavoidable reality, and for most employees, they are, in fact,
the most motivating parts of their jobs (Wiley, 1997).
Taken together, both the context in which the research has been conducted (i.e., pre-
dominantly laboratory studies) and the outcomes used in this research (i.e., free-choice
behavior and interest level) has led many to conclude that “it is time to move beyond the
undermining effect body of research” (Cerasoli et al., 2014, p. 981; see also Gerhart &
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Intrinsic motivation and creativity 107
Fang, 2015; Reiss, 2005). To this end, Cerasoli and colleagues (2014) conducted a meta-
analysis examining the joint and relative effects of intrinsic motivation and extrinsic moti-
vation on performance across three domains – school, work, and physical. Performance
type (quality vs quantity) and incentive contingency (directly performance salient vs
indirectly performance salient) were examined as moderators. The overall relationship
between intrinsic motivation and performance was r 5 0.26. Interestingly, when incen-
tives were present, this relationship strengthened, r 5 0.36 (without incentives, r 5 0.27).
Salience of incentives moderated this relationship such that indirectly salient incentives
(e.g., grades given at the end of the semester) strengthened the intrinsic motivation–per-
formance link (r 5 0.45), while directly salient incentives (e.g., rewards provided based on
performance in an experimental task) diminished it (r 5 0.30). Although no encompass-
ing theory was proposed to explain these findings, the authors offered a “crowding out”
hypothesis (Greene, 2011). This hypothesis suggests that when the link between incentives
and performance is weak or unclear (e.g., base salary), intrinsic motivation will be a better
predictor of performance and extrinsic motivation a weaker one. However, when the link
between incentives and performance is highly salient (e.g., commission on a sale), intrinsic
motivation will be a weaker predictor of performance “because incentives become the
more salient factor to performance” (Cerasoli et al., 2014, p. 983). Importantly, what this
hypothesis suggests is that intrinsic motivation and extrinsic motivation have an additive
effect, even though one or the other may be a stronger predictor of performance depend-
ing on certain conditions.
In addition to connecting intrinsic motivation and performance, another key gap
addressed by Cerasoli et al.’s (2014) meta-analysis was examining the type of performance
predicted by intrinsic and extrinsic motivation. When quality (rather than quantity) is the
performance criterion, intrinsic motivation is a much stronger predictor of performance
(b 5 0.35) than incentives (b 5 0.06). On the other hand, when quantity is the perfor-
mance criterion, incentives are a stronger predictor (b 5 0.33) than intrinsic motivation
(b 5 0.24). Finally, when these criteria are combined, intrinsic motivation and incentives
are equally predictive of performance (b 5 0.29).
These findings provide strong support for Amabile’s (1996) original proposition that
intrinsic motivation is important for creative performance (which was subsumed in
Cerasoli et al.’s [2014] “quality” performance category). However, these findings also
indicate that extrinsic motivation makes a positive contribution as well. This is in stark
contrast to the dominant perspective on motivating creativity, which still espouses the
undermining effect:
Past research examining the psychological factors that drive creativity has already yielded an
extensive body of literature showing that the main motivator of creative behavior is the creator’s
intrinsic interest and enjoyment . . . that intrinsic motivation enhances creativity, and extrinsic
motivation can harm creativity insofar as it decreases intrinsic motivation (for a review, see
Amabile, 1996). (Forgeard & Mecklenburg, 2013, p. 255)
Although some creativity researchers have begun to revisit this idea (e.g., Hennessey &
Amabile, 2010), the influence of the undermining effect hypothesis has had a tremendous
and lasting impact on this research area (e.g., Bergendahl, Magnusson, & Björk, 2015;
Erbas & Bas, 2015; Malik, Butt, & Choi, 2015). A great deal of research, particularly
with respect to contextual variables (e.g., leadership), has been oriented around intrinsic
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108 Handbook of research on leadership and creativity
motivation as the central mediating mechanism, often even taking it for granted that this
form of motivation will have a positive influence on creative performance.
Intrinsic Motivation as a Mediator
The functioning of intrinsic motivation as a mediator between contextual characteristics
and creative performance has seldom been directly examined (Shalley et al., 2004), and
when it has, the results have been inconsistent. For example, in a study of Chinese soft-
ware engineers, intrinsic motivation was tested as a mediator between empowering leader-
ship and employee creativity (Zhang & Bartol, 2010). Although the intrinsic motivation
was significantly related to creativity, the direct path between empowering leadership and
intrinsic motivation was non-significant. Similar results were observed in an experimental
study with undergraduate students (Shalley & Perry-Smith, 2001). While intrinsic motiva-
tion was a significant predictor of creativity, it was not found to be a significant mediator
of the evaluation–creativity relationship. In another study, Malik et al. (2015) observed
in a sample of Pakistani executives that intrinsic motivation significantly mediated the
relationship between extrinsic rewards and creative performance. However, in a sample
of Korean financial planners, intrinsic motivation was not a significant mediator between
rewards and creative performance (Yoon, Sung, Choi, Lee, & Kim, 2015).
Taken together, these results provide an ambiguous picture of the relationship between
intrinsic motivation and creativity. This observation has been by many others and gener-
ated several calls for gaining a better understanding of this relationship (e.g., Grant &
Berry, 2011; Montag et al., 2012; Shalley et al., 2004; Zhou & Hoever, 2014): “rather than
assume that intrinsic motivation underlies creativity, researchers need to tackle this theo-
rized linkage more directly and in more depth” (George, 2007, p. 445). In response to this
call, in the following section, we present a self-regulatory perspective on creativity. As a
more proximal theory of motivation (Kanfer, 1990), self-regulation may be able to shed
light on the mixed and inconsistent effects just described. Following a discussion of this
perspective, we then turn to the implications it has for leaders.
SELF-REGULATION: LINKING INTRINSIC MOTIVATION AND
CREATIVITY
Self-regulation
Self-regulation refers to processes that enable people to direct their affect, behavior, and
cognition over time to their desired end states (Karoly, 1993). Broadly speaking, most
theories of self-regulation propose that individuals set goals, evaluate their progress
against those goals, then modify their affect, behaviors, and cognitions to close the dis-
crepancy between their goals and current state (Carver & Scheier, 1998). At the center of
self-regulation is a negative feedback loop, a process that consists of four components: an
input function, a reference value, a comparator, and an output function. The input func-
tion is an assessment of one’s present level of performance. The reference value is a rep-
resentation of the desired state or goal. The comparator matches the input and reference
value to determine if the goal has been reached or exceeded or if one is short of the goal.
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Intrinsic motivation and creativity 109
Based on the results of this comparison, the output function will engage in order to bring
subsequent performance in closer alignment with the goal. In this phase of self-regulation,
individuals may change their strategy, level of effort, interpretation of the goal, among
other possible changes. This general model of self-regulation assumes that goals or stand-
ards exist in a hierarchical structure (Carver & Scheier, 1998). The hierarchical structure
suggests that lower-level, concrete goals are pursued in order to accomplish higher-level,
abstract goals (Lord & Levy, 1994). Importantly, in contrast to self-determination theory
(Gagné & Deci, 2005), self-regulation theory does not make any attempt to describe goal
contents; instead, the focus is on describing the processes relevant to goal establishment,
striving, and revision (Diefendorff & Lord, 2008).
Many models of self-regulation have been proposed (e.g., self-efficacy theory, Bandura
[1977]; control theory, Carver & Scheier [1981]; action regulation, Frese & Zapf [1994];
goal setting, Locke & Latham [1990]; and resource allocation, Kanfer & Ackerman
[1989]), and with these numerous models many constructs have been introduced that
are overlapping or closely related. Sitzmann and Ely (2011) conducted a meta-analytic
review of these constructs and provided an organizing framework grouping them into
three categories – regulatory agents, regulatory mechanisms, and regulatory appraisals. A
regulatory agent is something that initiates self-regulation; all major self-regulation theo-
ries view goals as the primary regulatory agent. Regulatory mechanisms are the second
category of constructs. These mechanisms are what drive how people make progress to
their goals. They include planning, monitoring, metacognition, attention, learning strate-
gies, persistence, time management, environmental structuring, help seeking, motivation,
emotion control, and effort. We will discuss these and their relevance to creativity later.
Finally, regulatory appraisals, as the third category of constructs, are variables relevant
to assessing goal progress and whether a person will decide to continue pursuing his
or her goals. Regulatory appraisal processes include self-evaluation, attributions, and
self-efficacy.
Self-regulation and Intrinsic Motivation
Evidence of how self-regulation theories and self-determination theory are related is sur-
prisingly sparse. Indeed, one of the few pieces of literature we could locate that addresses
both of these perspectives was published almost 25 years ago (Kanfer, 1992). In Kanfer’s
(1992) review of work motivation, she provides a heuristic framework of motivation
theories organized by proximity to behavior. From most distal to most proximal, the
framework is organized as follows: genetics/heredity, needs/personality/interests (e.g.,
achievement motivation), motives (e.g., cognitive evaluation theory, equity theory), cog-
nitive choice (e.g., expectancy theory, resource theory), intentions (e.g., image theory),
goals (e.g., goal-setting theory), and self-regulation (e.g., control theory, social cognitive
theory). As this framework illustrates, there are a number of levels of analysis that appear
to separate self-determination theory and self-regulation theories. Despite the lack of an
organized theoretical integration, there are numerous empirical studies that have identi-
fied various pieces of this complex puzzle.1
Empirical studies of intrinsic motivation have found that it is not only related to a number
of positive outcomes (e.g., job satisfaction, job performance), but also many important
processes, including higher levels of effort, persistence, concentration, engagement,
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110 Handbook of research on leadership and creativity
learning, cognitive processing, and use of metacognitive strategies (Pelletier, Fortier,
Vallerand, & Brière, 2001; Sheldon & Elliot, 1998; Turban, Tan, Brown, & Sheldon, 2007;
Vansteenkiste et al., 2010). A consistent theme throughout these studies, and as noted
in Kanfer’s (1992) framework, is the central role of goal setting. That is, intrinsic moti-
vation appears to translate into self-regulation (and, ultimately, action) through goals.
According to self-determination theory, for the intrinsically motivated person, the why of
goal pursuit is based in satisfying one’s needs for autonomy, competence, and relatedness
(Deci & Ryan, 2000). What about the contents (or the what) of goal pursuit? That is, what
goals are set by intrinsically motivated people, and what implications might this have for
the self-regulatory processes they are likely to utilize? Furthermore, does intrinsic motiva-
tion lead to different self-regulatory processes than extrinsic motivation, and might this
explain the differences in the outcomes of these two types of motivation?
To address these questions, we turn to theories of goal orientation. Similar to self-
determination theory, Nicholls (1984), Dweck (1986), and others (e.g., Ames, 1992; Elliot,
1997; Maehr & Midgley, 1991; Pintrich & Schunk, 1996) describe two forms of goal ori-
entation. One type of goal orientation is focused on demonstrating competence. Nicholls
refers to these goals as ego involved, and Dweck refers to them as performance goals.
The second type of goal orientation is about developing competence, which Nicholls
and Dweck refer to as task involved and learning goals, respectively. Dweck (1986) and
Nicholls (1984) have both theorized and found evidence to support the link between
intrinsic motivation and learning goals, as well as the link between extrinsic motiva-
tion and performance goals (see also Ntoumanis, 2001; Standage, Duda, & Ntoumanis,
2003). Indeed, there is evidence to support that goal orientation fully mediates the
relationship between performance and intrinsic/extrinsic motivation (Cerasoli & Ford,
2014). Although self-determination theory suggests that these types of motivation are
best conceptualized as varying along a continuum of autonomy, Deci and Ryan (2000)
readily acknowledge the convergence of evidence from self-determination theory and goal
orientation theories. Indeed, motivational researchers have previously noted the overlap
between these perspectives (e.g., Austin & Vancouver, 1996; Ford, 1992). For the purposes
of this chapter, this point is important on two fronts. First, a much larger body of research
exists on the relationships between goal orientation, self-regulation, and performance,
than does on the relationships between intrinsic/extrinsic motivation, self-regulation, and
performance. Second, goal orientation theories offer an additional theoretical dimension
that is not included in self-determination theory – namely, a distinction between approach
(or promotion) and avoidance (or prevention) goals (Elliot, 1997).
Self-regulation and Goal Orientation
Although a number of terms have been used to describe the distinction between goal ori-
entations, we will follow Dweck’s terminology here. Learning goals focus on progress and
mastery of a task, with the objective being to increase one’s level of competence (Dweck,
1986). In contrast, performance goals focus on managing how others perceive one’s level
of competence, with the objective being to be seen as competent and avoid being seen as
incompetent (Dweck, 1986). Elliot (1999) extended this framework by proposing that a
performance goal orientation can be further broken down into performance-approach
and performance-avoid goal orientations. Performance-approach goals emphasize the
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Intrinsic motivation and creativity 111
desired possibility of proving or demonstrating one’s high level competence to others,
while performance-avoid goals emphasize the undesired possibility of proving or demon-
strating one’s low level of competence to others.
A great deal of research has been conducted examining the ways in which these three
types of goal orientations relate to self-regulation and learning and performance out-
comes. Generally, learning goals have been shown to have the strongest positive impact.
Previous research has demonstrated that learning goals are associated with viewing
difficulty as a challenge rather than a threat, absorption in a task, effort, self-regulated
learning, deep processing of information, and long-term retention of information (Elliot,
1999, 2006; Elliot & Church, 1997; Elliot & Harackiewicz, 1996; Elliot & McGregor, 1999;
McGregor & Elliot, 2002). In contrast, performance-avoid goals are frequently linked
with negative processes and outcomes. These include viewing difficulty as a threat, low
absorption in a task, higher levels of anxiety, less self-regulated learning, unwillingness
to seek help, shallow processing of information, fear of evaluation, and poor retention
of information (Elliot, 1999, 2006; Elliot & Church, 1997; Elliot & Harackiewicz, 1996;
Elliot & McGregor, 1999; Middleton & Midgley, 1997). Performance-approach goals
have been shown to be largely related to positive processes and outcomes, but they are
also associated with a few negative consequences. On the one hand, like learning goals,
performance-approach goals are associated with viewing difficulty as a challenge rather
than a threat, absorption in a task, persistence, and effort (Elliot & Church, 1997; Elliot&
Harackiewicz; McGregor & Elliot, 2002). On the other hand, performance-approach
goals are associated with anxiety during evaluation and shallow processing of infor-
mation (Elliot, 1999, 2006; Elliot, McGregor, & Gable, 1999). The mixed findings on
performance-approach goals (Elliot & Moller, 2003) has led some to conclude that an
optimal goal orientation profile is a combination of mostly learning goals and a modest
amount of performance-approach goals (Pintrich & Garcia, 1991). However, others have
suggested that a strong presence of both types of goals is optimal for effective processes
and desirable outcomes, including creativity (Ainley, 1993; Bouffard, Boisvert, Vezeau, &
Larouche, 1995; Farr, Hofmann, & Ringenbach, 1993; Seijts & Latham, 2012; Wentzel,
1991).
Goal Orientation and Performance
Although the majority of the research on goal orientation has taken place in educational
contexts, it was introduced into the organizational literature in the early 1990s (Farr et
al., 1993; Kanfer, 1990; Sujan, Weitz, & Kumar, 1994). In this literature, goal orientation
has been studied as an antecedent to a number of training outcomes, such as declarative
knowledge, procedural knowledge, self-efficacy, training performance, and transfer (e.g.,
Brett & VandeWalle, 1999; Button, Mathieu, & Zajac, 1996; Ford, Smith, Weissbein,
Gully, & Salas, 1998; Heimbeck, Frese, Sonnentag, & Keith, 2003; Kozlowski et al., 2001;
Schmidt & Ford, 2003). More recently, goal orientation has been examined as a predictor
of job performance. For example, in a study of salespeople (Porath & Bateman, 2006),
learning goal orientation (r 5 0.28) and performance-approach goal orientation (r 5 0.30)
were positively related to meeting one’s sales quota, while performance-avoid goal orienta-
tion was negatively related (r 5 –0.29). This pattern of results is generally consistent with
what has been observed in educational contexts (e.g., Cury, Elliot, Da Fonseca, & Moller,
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112 Handbook of research on leadership and creativity
2006). However, evidence for the link between goal orientation and job performance has
been mixed. For instance, in a sample of Dutch workers at an energy supply firm, Janssen
and Van Yperen (2004) found that learning orientation was positively related to job per-
formance (r 5 0.20), but performance-approach orientation was unrelated (r 5 –0.01).
Another study observed that neither learning (r 5 0.12) nor performance-approach (r5
0.05) orientation were related to job performance, but performance-avoid orientation
was significantly negatively related (r 5 –0.14) (Whitaker & Levy, 2012). Despite this
variability, a meta-analysis observed the expected relationships between goal orientation
and job performance held (Payne, Youngcourt, & Beaubien, 2007): learning (r 5 0.15),
performance approach (r 5 0.09), and performance avoid (r 5 –0.06).
The variability in findings may be attributable to differences in contexts, but others
have offered a different perspective. Yeo, Loft, Xiao, and Kiewitz (2009) suggested and
found supporting evidence for the idea that the relationship between goal orientation and
performance fluctuates across levels of analysis and task demands. Specifically, Yeo et
al. (2009) observed that while a learning orientation did not predict performance at the
between-person level, it was positively related to performance at the within-person level.
In addition, performance-approach and performance-avoid orientations were positively
and negatively related, respectively, to performance at the between-person level, but, inter-
estingly, the effect of a performance-approach flipped as a function of task demands. That
is, as the task became more complex, requiring deeper levels of learning to perform well,
a performance-approach orientation was negatively related to performance at the within-
person level. In sum, for the purposes of this chapter, a few important implications can
be drawn from Yeo et al.’s (2009) findings. First, it is imperative to examine motivational
processes at the within-person level. This is reflective of a broader trend in workplace
research (Dalal, Bhave, & Fiset, 2014; Lord, Diefendorff, Schmidt, & Hall, 2010). Second,
even if levels of learning orientation do not predict performance at the between-person
level, it appears to enhance performance at the within-person level, which suggests that
individuals are likely to do their best work when they adopt a high level of learning
orientation relative to their own average level. Thus, if a person has a low level of trait
learning orientation, this does not disqualify him or her from doing creative work. On the
contrary, what this finding suggests is that regardless of individuals’ level of trait learning
orientation, if a leader can find a way to enhance their subordinates’ state learning orien-
tation, gains in performance can be expected. Third, at the between-person level, higher
levels of performance-approach orientation are beneficial to performance; however, as
task demands increase (e.g., when a task is new or complex), increases of a performance-
approach orientation within an individual can be detrimental to performance. In other
words, a person who is characteristically focused on demonstrating his or her competence
or abilities to others (i.e., has a high level of trait performance-approach orientation) is
likely to outperform those who rarely think about demonstrating their competence or
abilities. However, this comes with a caveat. When a task is new or complex, individu-
als should be encouraged to focus on learning the task (i.e., adopt a high level of state
learning orientation), not demonstrating their abilities. To the extent that people focus on
demonstrating their abilities when working on a new or complex task (i.e., adopt a high
level of state performance-approach orientation), their performance is likely to suffer.
This final point highlights the importance of understanding and carefully defining the
criterion of interest (Austin & Villanova, 1992). In Payne et al.’s (2007) meta-analysis,
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Intrinsic motivation and creativity 113
they examined the effects of goal orientation on task and job performance. There is a
large body of evidence that suggests creative performance differs from task performance
and, as such, should be treated as a unique criterion (e.g., Ford, 1996; Pulakos, Arad,
Donovan,& Plamondon, 2000). We turn now to the evidence on the link between goal
orientation and creative performance. In one of the first studies on this topic, Janssen
and Van Yperen (2004) found that learning orientation was positively related to creative
performance (r 5 0.21), which was operationalized as a composite of idea generation, idea
promotion, and idea implementation behaviors. Performance-approach orientation was
also positively related but non-significantly (r 5 0.06). In a study of Taiwanese insurance
agents, Gong, Huang, and Farh (2009) observed a similar relationship between learning
orientation and creative performance (r 5 0.20), as did Hirst, Van Knippenberg, and
Zhou (2009) in a cross-national sample of R&D teams in the pharmaceutical industry
(r 5 0.25). In addition, Hirst et al. (2009) found non-significant relationships between
performance-avoid and performance-approach orientations and creativity (r 5 –0.14 and
r 5 0.08, respectively). Thus, although only a few studies are available, the extant evidence
is forming a relatively consistent pattern. Namely, learning orientation appears to be posi-
tively related to creative performance, while performance-approach and performance-
avoid orientations are positively and negatively related, respectively, but non-significantly.
Interestingly, a number of the studies examining the relationship between goal orienta-
tion and performance – be it task, contextual, or creative – identify self-regulation as a
critical mediator. For example, VandeWalle, Brown, Cron, and Slocum (1999) found that
the positive effect of learning orientation on sales performance was fully mediated by three
self-regulatory processes: goal-setting, intended planning, and intended effort. Similarly,
Porath and Bateman (2006) observed that proactive behavior, emotional control, and
social competence fully mediated the relationships between goal orientation (in this case,
learning and performance-approach) and sales performance. With respect to creative
performance, knowledge sharing, self-efficacy, feedback seeking, and exploration have all
been identified as significant mediators (De Stobbeleir, Ashford, & Buyens, 2011; Gong
et al., 2009; Hirst, Van Knippenberg, Zhou, Zhu, & Tsai, 2015; Lu, Lin, & Leung, 2012).
Taken together, there is growing body of evidence demonstrating not only the presence
of a relationship between goal orientation and performance, but also how these variables
are connected – namely, through self-regulation. Of course, while a between-person
perspective provides valuable insights, self-regulation is fundamentally a within-person
phenomenon and thus should be examined at this level (e.g., Yeo et al., 2009). The recent
debate surrounding the effects of self-efficacy serves as a notable illustration of this point,
a topic we will return to shortly. Research on creativity at the within-person level is gener-
ally sparse (cf. Ruscio, Whitney, & Amabile, 1998); furthermore, some of the research that
does exist at this level has too few or too infrequent measurement occasions to adequately
capture fluctuations in creative behaviors and the self-regulatory processes that contrib-
ute to this variability (e.g., Tierney & Farmer, 2011). Therefore, in the following sections,
by drawing heavily on the self-regulation literature, we seek to accomplish the following
two objectives. First, we will suggest possible ways in which self-regulatory mechanisms
influence creative behaviors and outcomes. Second, we discuss the implications of these
propositions for the leaders of creative efforts.
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114 Handbook of research on leadership and creativity
Self-regulation and Creative Processes
Recall that this chapter began by tracing the roots of the intrinsic motivation principle
of creativity (Amabile, 1997), which led us to cognitive evaluation theory (Deci, 1975)
and self-determination theory (Deci & Ryan, 1985; Gagné & Deci, 2005). Because little
empirical or theoretical evidence was available showing how intrinsic motivation was
linked to creative performance, we looked to a very similar but more proximal theory
of motivation to explain this connection – namely, goal orientation theory (Ames, 1992;
Dweck & Leggett, 1988; Nicholls, 1984). Goal orientation theory and research have been
tightly linked with self-regulation theories, which are the most proximal explanations of
human behavior (Kanfer, 1992), and as such, provide valuable insight into how intrinsic
motivation is linked to creative performance.
This leads us to the following questions: How are the self-regulatory processes induced
by a learning orientation or intrinsic motivation different from the processes induced by
a performance-approach orientation or extrinsic motivation? Are the processes associ-
ated with learning orientation always preferred, or are there conditions under which a
performance-approach orientation could prove beneficial? Ultimately, how do these pro-
cesses affect creative behaviors and outcomes? In answering these questions, we focus on
four self-regulatory processes that have received some of the most attention – self-efficacy,
metacognition, learning strategies, and feedback seeking.
Learning orientation is generally associated with higher levels of self-regulation
(Pintrich & Schunk, 1996). Because self-regulatory processes tend to be highly correlated
(Sitzmann & Ely, 2011), it is not surprising that past research has demonstrated that
learning orientation is positively associated with each of the four processes mentioned
previously. These consistently higher levels of self-regulation provide an explanation for
why learning orientation enhances performance across numerous domains and contexts.
That being said, it is not well understood how the self-regulatory processes relate to each
other and operate to influence performance over time. Recent research, however, is begin-
ning to shed light on this. Broadly speaking, self-regulatory processes can be grouped into
exploratory or exploitative processes (Gupta, Smith, & Shalley, 2006; Mehlhorn et al.,
2015). Exploratory processes, such as feedback seeking and deep learning strategies, are
associated with switching strategies, discovery, and acquiring information. In contrast,
exploitative processes, such as self-efficacy and metacognition, are associated with refin-
ing existing strategies and optimizing performance.
These two sets of processes are likely to be beneficial at different stages of performance
and problem solving, just as “different goal orientations may be called for at different
phases of skill acquisition” (Kanfer, 1990, p. 236). When a task is novel, complex, and ill
defined –such as those requiring creativity (Mumford & Gustafson, 1988) – performance
early on requires substantial effort and attentional resources (Anderson, 1982; Kanfer &
Ackerman, 1989). As novelty and complexity increase, more time will have to be spent
understanding the task. The purpose of this phase of performance is to develop a cog-
nitive representation or mental model of the problem (Johnson-Laird, 1983; Kanfer &
Ackerman, 1989). Similarly, the first stage of creative problem solving deals with defining
the problem or opportunity requiring a new solution (Amabile, 1996; Mumford et al.,
1991).
It is during this initial stage of creative performance that exploratory self-regulatory
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Intrinsic motivation and creativity 115
processes may be most critical; particularly the problem to be solved must first be discov-
ered (Unsworth, 2001). Problem discovery –and, by extension, problem definition– begins
with domain-relevant knowledge (Amabile, 1983b; Mumford et al., 1991). Knowledge is
gained by organizing information into categorical structures (Chi, Glaser, & Rees, 1982).
To discover complex problems, a deep understanding of information is required. Previous
research has shown that one reason expertise facilitates creativity is due to how informa-
tion is identified, encoded, and organized in knowledge structures (Ericsson & Charness,
1994; Mumford, Baughman, Supinski, & Maher, 1996). The learning strategies that
develop the kind of knowledge structures required for creativity are referred to as “deep
learning strategies” (Pintrich, Roeser, & De Groot, 1994). These deep learning strategies,
in contrast to surface learning strategies, are commonly associated with a learning goal
orientation (Pintrich, 2000). They are also largely exploratory in nature. Deep learning
strategies, such as elaboration, are used to better understand information by finding
connections to previously acquired knowledge, thinking through its implications, and
identifying anomalies (Levin, 1988).
Exploratory self-regulatory processes are likely to continue to be beneficial after a
problem has been defined. Following problem definition is an information-gathering
or preparation stage (Amabile, 1996; Mumford et al., 1991). Gathering information is a
quintessential exploratory behavior (Mehlhorn et al., 2015). This process can occur inter-
nally by accessing one’s memory or externally by seeking new sources. Feedback-seeking
behavior is one means through which a person can gather new, external information.
Previous research has not only identified feedback seeking as related to a learning orienta-
tion (Anseel, Beatty, Shen, Lievens, & Sackett, 2015; Porath & Bateman, 2006), but also
as a significant predictor of creative performance (De Stobbeleir et al., 2011). Thus, there
seems to be some support for the importance of exploratory self-regulation processes in
the first two stages of creative problem solving.
It should be noted, however, that more is not always better (Grant & Schwartz,
2011; Keith, Unger, Rauch, & Frese, 2015; Pierce & Aguinis, 2013). Depending on
the scope and clarity of the problem definition, the information-gathering stage can
require extensive time and resources. These demands emerge from a need to efficiently
categorize new information so that it is readily accessible when solutions must be gen-
erated. For a complex and ill-defined problem, this information-gathering process can
easily extend beyond a point of diminishing returns. For a person with a high learning
orientation, this may not be seen as problematic. Typically, a learning orientation is
characterized by positive affect, interest, and engagement (Dweck & Leggett, 1988).
When this motivational profile is paired with a goal of mastering an extraordinarily
complex domain, the search for information can ultimately turn unproductive. Once
a problem or task is understood “well enough,” more exploitative self-regulatory
processes can be beneficial for accomplishing organizational goals. The decision con-
cerning when a person or team has reached a “good enough” understanding is surely
based on a number of factors. One of these is the target type of creativity and creative
outcome. For example, radical creativity, as opposed to incremental, is more likely to
occur when more disparate bodies of knowledge are brought together (Gilson, Lim,
D’Innocenzo, & Moye, 2012; Simonton, 2003). For this to take place, one can expect
that many more resources will have to be devoted to information gathering. Similarly,
for highly novel creative outcomes, extensive information gathering may be necessary.
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116 Handbook of research on leadership and creativity
Incremental creativity, on the other hand, may require fewer resources to be devoted
to gathering information.
As a person or team transitions into the idea generation stage, the information that has
been gathered is put to use. In this stage of creative performance, possible solutions for
solving the problem at hand are produced (Litchfield, 2008). These ideas are then evalu-
ated or tested to determine the extent to which they meet the demands of the problem at
hand. When solving a complex problem, this process of generation then evaluating can
require many iterations (Amabile, 1996). Information that is discovered in the evaluation
stage can even lead a person or team to revisit how the problem was defined at the outset
(Amabile, 1996; Mumford, Medeiros, & Partlow, 2012). This iterative cycle is most effec-
tively executed when a learning orientation is adopted, particularly early in the develop-
ment of a new product or process. In Osborn’s (1957) original work on brainstorming,
one of the four rules was to combine and improve on previous ideas. Exploratory self-
regulation processes, such as the deep learning strategy of elaboration, fits nicely with
this. Furthermore, after ideas have been produced, seeking feedback, another exploratory
process, from a project leader or external stakeholder can facilitate the continued develop-
ment and refinement of these ideas (Holman et al., 2012). With a learning orientation,
feedback is likely to be seen as facilitating progress, which, in turn, produces higher levels
of task engagement (Pintrich, 2000). This is congruent with self-determination theory in
that informational evaluation is thought to foster intrinsic motivation and creative per-
formance (Deci & Ryan, 1985; Shalley & Perry-Smith, 2001).
Given the evidence cited here, it is not surprising that a learning orientation, and
the exploratory self-regulatory processes it produces, is strongly linked to creative per-
formance. However, there is a time and place for a performance orientation as well. In
fact, recent experimental evidence has shown that is the combination of learning and
performance-approach orientations that leads to the best creative outcomes (Miron-
Spektor & Beenen, 2015). With this in mind, we now turn to an examination of two
exploitative self-regulatory processes –self-efficacy and metacognition. A large body of
evidence exists in the creativity and management literatures linking self-efficacy – defined
as the belief that one can achieve a particular level of performance (Bandura, 1997) – and
idea generation. Although frequently referred to as “creative self-efficacy,” an examina-
tion of the items from the most widely used measure (i.e., Tierney & Farmer, 2002) reveals
a clear emphasis on idea generation (e.g., “I have a lot of good ideas”) (Ng & Lucianetti,
2016). A recent meta-analysis showed a strong link (r 5 0.40) between self-efficacy and
various creative performance variables (Bjornberg & Davis, 2015). However, as noted
earlier, self-efficacy is more appropriately examined at the within-person level since it is a
dynamic, within-person phenomenon. To our knowledge, no evidence exists at this level
on the function of self-efficacy in creative performance. This point is important, not just
for the sake of aligning theory and measurement, but because using different levels of
analyses can lead to very different conclusions. Indeed, in regards to self-efficacy, this has
certainly been the case.
Over the past 20 years, a debate has been unfolding around the nature of the relation-
ship between self-efficacy and resource allocation (Bandura, 2012, 2015; Vancouver,
2012). Proponents of social cognitive theory (Bandura, 1986, 1997) argue that self-
efficacy beliefs benefit goal striving by signaling to allocate more resources (i.e., time and
effort) to a task when one is near goal achievement. On the other hand, from the per-
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Intrinsic motivation and creativity 117
spective of control theory (Powers, 1973, 1991; Vancouver and Day, 2005), higher levels
of self-efficacy signal to allocate fewer resources to goal striving when a goal is readily
achievable (Vancouver, Weinhardt, & Schmidt, 2010). These resources can then be con-
served or reallocated to more pressing goals (Beck & Schmidt, 2015). Thus, while social
cognitive theory predicts that self-efficacy will have a positive impact on goal-striving
performance, control theory suggests that self-efficacy will only contribute to goal striving
when goal progress is ambiguous (Vancouver, More, & Yoder, 2008). When goal progress
is unambiguous, self-efficacy will have a negative effect on resource allocation and per-
formance (Halper and Vancouver, 2016; Schmidt & DeShon, 2010). Because the negative
effects of self-efficacy are primarily observable at the within-person level (cf. Vancouver,
Gullekson, Morse, & Warren, 2014), it is not surprising that a large body of research has
found a positive relationship at the between-person level with respect to self-efficacy and
performance (Stajkovic & Luthans, 1998) and learning (Sitzmann & Ely, 2011). When
examined at the within-person level, however, meta-analytic evidence supports control
theory’s predictions (Sitzmann & Yeo, 2013). The positive between-person relationship
is due to the influence that performance has on self-efficacy beliefs, rather than the other
way around (Heggestad & Kanfer, 2005).
Despite the negative impact of self-efficacy on performance, when viewed from a
resource allocation perspective, this can be seen as an adaptive process (Beck & Schmidt,
2015). Resources can be devoted to a number of different activities, such as on-task,
off-task, and self-regulatory activities (Yeo & Neal, 2004). Typically, in studies of self-
efficacy (e.g., Beck & Schmidt, 2012, 2015), its effect on effort intensity or persistence
is examined, but effort consists of a third component – direction (Pinder, 2008; Vroom,
1964). Within a complex task, a person must decide how to allocate one’s resources
among multiple goals (Schmidt & DeShon, 2007; Schmidt, Dolis, & Tolli, 2009). Some
of these task goals are more exploratory in nature (e.g., discovering new strategies), while
others are more exploitative in nature (e.g., performing at a high level). Recent research
has shown that while self-efficacy is generally negatively related to resource allocation,
this is particularly pronounced for efforts directed at exploration (Hardy, Day, Steele,
Westlin, & Nguyen, 2016). In some respects, this can be seen as adaptive. As individu-
als become more confident in their understanding of a task, they are better served by
allocating resources to performing, rather than learning (Kanfer & Ackerman, 1989).
However, people tend to poorly estimate their level of competence and understanding
(Carter & Dunning, 2008; Kruger & Dunning, 1999). If individuals inaccurately assess
their level of competence, they prematurely divert resources away from learning and
exploration.
In the case of creative performance, the mixed effects associated with self-efficacy need
to be considered carefully. On the one hand, self-efficacy is needed to accept a goal and
engage a task in the first place (Vancouver et al., 2008). In addition, self-efficacy can serve
as a signal (albeit, a potentially unreliable one) for when one has made satisfactory pro-
gress towards solving one aspect of a problem and may move on to another. On the other
hand, self-efficacy leads to reduced levels of effort, especially towards learning-oriented
goals (Hardy et al., 2016). Furthermore, when performance feedback is ambiguous (i.e.,
goal progress cannot be clearly assessed), as is frequently the case in creative problem
solving, the negative relationship between self-efficacy and effort becomes accentuated
(Halper & Vancouver, 2016; Schmidt & DeShon, 2010). Highly efficacious people may
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118 Handbook of research on leadership and creativity
feel confident in sharing their ideas, but these ideas could be based on a problem that
was not thoroughly understood and on information that was not painstakingly gathered.
Highly efficacious people may also be less willing to seek out feedback due to their con-
fidence in their level of performance (Hardy, Day, & Steele, 2016).
Similar to self-efficacy, metacognition is an exploitative self-regulatory process. Unlike
the deep learning strategies described earlier (e.g., elaboration, organization), which
lead to discovering new information or strategies, metacognition helps to refine existing
goal-directed behavior (Bell & Kozlowski, 2010). By monitoring and planning various
aspects of the task, self, and context (Pintrich, Wolters, & Baxter, 2000), people engaging
in metacognition can identify what has contributed to success or failure and modify their
approach accordingly (Ford et al., 1998). Depending on the type of creativity of interest,
metacognition may be best employed at different stages of the creative process. For incre-
mental or expected creativity, metacognitive activity should be high at the outset. Because
metacognition builds on the known and familiar, the idea or product that results from
high levels of early metacognitive activity is likely to be more closely related to the starting
point (Berg, 2014; Litchfield, 2008). In contrast, when radical or highly novel creativity is
sought, metacognition should be delayed at the outset. As noted earlier, radical creativity
is usually linked to the combining of disparate areas of knowledge (Simonton, 2003). For
this type of creativity, metacognition may best serve as a way to refine an absurd idea into
something useful. This can be done by taking what is known about the creative idea and
modifying it according to the practical constraints of which an individual is also aware
(Berg, 2014).
IMPLICATIONS FOR LEADERS
To be sure, the relationship between motivation and creativity described here presents a
considerable challenge for leaders. Leaders of creative endeavors must know the appro-
priate times to reward their subordinates and the type of reward to use. They must be
able to facilitate the interests of their subordinates while keeping in mind organizational
goals and values. They must be able to systematically navigate ambiguous and ill-defined
problems, being able to discern at what point a team should cease exploring and begin
capitalizing on what is known. In sum, the leaders of creative efforts must be skilled in
managing paradoxes (Hunter, Thoroughgood, Myer, & Ligon, 2011; Rosing, Frese, &
Bausch, 2011), and motivating creativity is no exception to that.
In this domain of research, the topic that has probably been the most contentious is
the use of rewards. It has long been thought that rewards are detrimental to creativity
(Amabile, 1996), due to the undermining effect that extrinsic rewards have on intrinsic
motivation (Deci et al., 1999). In light of recent evidence (e.g., Cerasoli et al., 2014),
however, the undermining effect is being revisited and boundary conditions more care-
fully examined (Hewett & Conway, 2016; Malik et al., 2015). In this chapter, we offer
an alternative perspective on rewards, using goal orientation theory. From this perspec-
tive, rewards influence creativity by constraining an individual’s focus to a particular
performance goal. What this suggests is that rewards are not necessarily detrimental to
creative performance; in fact, they can be beneficial, depending on when and what type
of reward is provided (Byron & Khazanchi, 2012). Performance goals foster the use of
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Intrinsic motivation and creativity 119
more exploitative self-regulatory strategies, which means leveraging known strategies
and approaches that have been successful. When a person is offered a reward that is
contingent on performance, he or she will adopt the most effective known strategy to
earn that reward. By definition, a known strategy is not a novel one, and thus will not
meet the criteria of being creative (i.e., novel and useful). It should not be surprising that
performance- contingent rewards do not lead to creativity, given that creativity requires a
departure from the routine and habitual (Ford, 1996). In other words, creativity requires
exploration, something that performance-contingent rewards – and the performance-
approach goal orientation these rewards elicit – discourage. However, different results
would be expected if rewards were explicitly made contingent on creative performance.
By requiring originality to obtain the reward, a person is compelled to suspend routine
approaches and explore something new. Indeed, meta-analytic evidence has found
support for the positive effects of creativity-contingent rewards on creative performance
in both field and experimental settings (Byron & Khazanchi, 2012). Nevertheless, leaders
should bear in mind that using creativity-contingent rewards will likely constrain the ideas
or products generated by their subordinates. Rewards prompt a person to look for the
most efficient means to achieve the objective. Even if the criterion for success includes an
originality component, people will look for a solution that is original enough to qualify for
the reward. Thus, with creativity-contingent rewards, leaders can only expect solutions of
modest or moderate novelty. This is line with the meta-analytic findings reported earlier,
where the relationship between creativity-contingent rewards and creative performance
was r 5 0.07 (Byron & Khazanchi, 2012). Of course, we are not suggesting that rewards
should be avoided. On the contrary, the evidence we have reviewed makes a strong case
for the benefits of rewards. What is advocated here is for leaders to simply bear in mind
the varying effects that different types of rewards are likely to produce. Constraints on
subordinates’ exploration or the level of novelty of their ideas or products can certainly
be desirable in some contexts. Radical creativity, if adopted, is very disruptive to organi-
zations (Damanpour, 1991) and is also more likely to fail and consume more resources
(Smith & Tushman, 2005).
On the other hand, radical creativity may have greater potential to enhance organiza-
tional performance (Benner & Tushman, 2003). If this is the type of creativity desired,
a leader may be better off avoiding rewards, because of their association with a perfor-
mance orientation, and focusing instead on fostering a learning orientation. Learning
goals, in contrast to performance goals, draw attention and resources away from the
outcome and focus them on the process (Kanfer & Ackerman, 1989; Seijts, Latham,
Tasa,& Latham, 2004). Exploration and knowledge acquisition are then seen as ends in
themselves, rather than as means. Leaders can facilitate the adoption of learning goals
through shaping the climate of their work unit (Kozlowski & Doherty, 1989; Mumford,
Scott, Gaddis, & Strange, 2002; Naumann & Bennett, 2000; Scott & Bruce, 1994).
Dragoni (2005) theorized that leaders can promote a learning orientation by prioritizing
employee development. Behaviors that would signal that development is a priority include
providing time off to engage in developmental activities, encouraging the application of
newly learned skills on the job, providing constructive feedback, encouraging experi-
mentation with new approaches, conveying the importance of learning from mistakes,
assigning jobs to stretch employees, and making resources that facilitate learning avail-
able (Cannon & Edmondson, 2001; Edmondson, 1996; Ford, Quiñones, Sego, & Sorra,
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120 Handbook of research on leadership and creativity
1992; Kozlowski& Farr, 1988; Maurer & Tarulli, 1994; McCauley, 2001; Van Velsor,
McCauley,& Moxley, 1998).
In addition to promoting a learning orientation, leaders can also take steps to insulate
their subordinates’ learning orientation if it is already present at a high level. Recent
research has shown that under conditions of high time pressure, people tend to adopt a
performance-avoid goal orientation, which then negatively impacts performance (Beck &
Schmidt, 2013). In contrast, lower levels of time pressure contribute to a higher learning
orientation, which, at the within-person level (Yeo et al., 2009), improves performance
(Beck & Schmidt, 2013). These findings on time pressure may come as a surprise to some.
Citing examples such as the Apollo 13 crisis, some leaders of creative efforts believe that
people will do their best and most creative work when the pressure to perform is high.
At other times, time pressure is not introduced voluntarily; it is simply a reality of the
workplace. Regardless of the source of time pressure, the evidence generally suggests that
time pressure is not beneficial to creative performance. The impact of time pressure on
goal orientation provides an explanation as to why this is the case (Beck & Schmidt, 2013).
Again, it is important to keep in mind the leader’s objectives. Learning and develop-
ment are clearly vital for creative performance. As such, exploratory behaviors should be
encouraged. However, exploration is not always what is needed, and sometimes, it simply
cannot be afforded (Chadwick & Raver, 2015; Gupta et al., 2006; March, 1991). If the
objective is to refine existing ideas for known demands or to produce a quick result, time
pressure may be a useful tool for a leader. Some research indicates that time pressure can
indeed be beneficial in the idea evaluation stage and in translating a creative idea into
innovation. When in the idea implementation phase, for example, time pressure may be
helpful because it helps to bring focus to relevant work goals and the tasks that need to be
done to achieve those goals. Additionally, time pressure may help to motivate members of
a team to come to a consensus (Kontoghiorghes, Awbre, & Feurig, 2005). Without dead-
lines, consensus may never be achieved when solving an ill-defined problem. Divergence
in thought early in the problem-solving process has been shown to lead to better solutions
(Brown & Paulus, 2002; Osborn, 1957; Reiter-Palmon, Mumford, O’Connor Boes, &
Runco, 1997), but it is important that this early period of divergence is followed by conver-
gence. McComb, Cagan, and Kotovsky (2015) demonstrated this in an experimental study
in which teams of undergraduate engineering students were asked to design a bridge,
given certain constraints. In this study, the teams that performed best explored multiple
ideas early on but then came to a consensus and worked to refine the design on which
consensus was reached. Multiple field studies have observed a similar pattern. In a five-
year study of 100 scientists and engineers, Andrews and Farris (1972) observed that time
pressure was positively related to the implementation of creative ideas (i.e., innovation).
Similarly, in a study of 81 workers from various industries, time pressure was positively
related to idea implementation, a relationship that was strengthened by the presence of
supervisor feedback (Noefer, Stegmaier, Molter, & Sonntag, 2009).
The research on time pressure highlights the importance of leaders having an intimate
knowledge of their subordinates’ readiness for executing creative performance behaviors
at the various stages of creative problem solving (Caniëls, De Stobbeleir, & De Clippeleer,
2014). However, the research on creative self-efficacy seems to have ignored this thus far.
The complexity of self-efficacy’s relationships with resource allocation and performance
are just beginning to be unpacked (Beck & Schmidt, 2012, 2015; Halper & Vancouver,
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Intrinsic motivation and creativity 121
2016; Hardy, 2014; Hardy et al., 2016; Schmidt & DeShon, 2009, 2010), but what is clear
is that these are not simple, linear relationships, as they are commonly presumed to be.
As a result, it is necessary for leaders to carefully consider context, desired outcomes,
and subordinates’ individual differences before intervening to influence self-efficacy. The
primary function is not to enable people to perform at their best. Instead, self-efficacy
serves as a signal for how to effectively allocate intrapersonal resources among multiple,
competing goals (Vancouver et al., 2010). Consequently, we argue that leaders should not
focus on increasing or decreasing their subordinates’ level of self-efficacy. Instead, we
suggest that leaders leverage the adaptive functioning of self-efficacy by setting specific,
actionable goals and providing frequent, clear feedback that will enable their subordinates
to accurately calibrate their level of performance relative to their goals (Bandura, 2012;
Schmidt & DeShon, 2010).
Of course, a question that may come to mind is, how does a leader set goals for achiev-
ing an outcome that is not fully known? Litchfield’s (2008) goal-based view of idea
generation provides some guidance on this. If the objective is radical creativity, leaders
should provide relevant parameters within which an idea must work, but then specify
that the ideas should be as novel or “foolish” as possible (March, 1976). Alternatively, if
the objective is incremental creativity, leaders should frame it as such – the goal ought to
specify that little should be changed about the existing product (i.e., low novelty), but it
needs to be made 5 percent more efficient (i.e., higher usefulness). The first iteration of
ideas is unlikely to produce what will become the final solution, but ideas can be shaped
and refined to better suit the demands at hand (Berg, 2014). Thus, a leader must be skilled
in providing effective feedback that will guide one’s subordinates to a successful outcome.
CONCLUSION
The motivational profile of creativity described in this chapter reflects the highly dynamic
nature of creativity itself. As such, there is not a one-size-fits-all solution, which is why
much of what is written here boils down to an argument for greater specificity – specific-
ity in motivational mechanisms (Gupta et al., 2006; Hardy et al., 2016), specificity in the
type of creativity (Unsworth, 2001), specificity in creative outcomes (Gilson et al., 2012;
Runco & Jaeger, 2012; Sullivan & Ford, 2010), specificity in creative behaviors (Amabile,
1996; Montag et al., 2012; Mumford et al., 1991), and specificity in the timing of the
creative process (Shalley & Gilson, 2004). It is not enough to have followers who are
motivated; motivation needs a direction (Dalal & Hulin, 2008). Leaders must know what
the objectives are, even if they change and develop over time, in order to effectively foster
and direct the efforts of their followers. They must also have an understanding of how
any motivational interventions are likely to interact with the personality and value profiles
of their subordinates (Feist, 1998; Feist & Barron, 2003). While still a work in progress,
a great deal of research has been conducted on creativity (for a review, see Mainemelis,
Kark, & Epitropaki, 2015) that can enable leaders of creative efforts to avoid having to
treat creativity and its motivational antecedents as a black box. We hope that the present
effort will generate productive discussion and examination of the propositions presented
here, and that it will benefit those who choose to put it into practice.
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122 Handbook of research on leadership and creativity
NOTE
1. It important to note that there is a difference in how self-determination theorists use the term “self- regulation”
and how self-regulation theorists use this term. Put simply, while theories of self-regulation emphasize
regulatory processes, self-determination theorists focus on the self. According to self-determination theory,
self-regulation is a continuum of autonomy (Deci & Ryan, 1985), rather than a set of interrelated processes.
Self-determination theory suggests that a high level of self-regulation involves behaviors that are executed
because a person is interested in the task or enjoys it, while a low level of self-regulation involves behaviors
that are executed because a person feels compelled by an external force (Deci, Ryan, & Williams, 1996). It is
noteworthy that this view of self-regulation describes the why of a behavior but not how, which is clearly a
different conception of self-regulation than the general model described in the previous section.
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