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The Building Blocks of Motivation

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Motivation and goals not only play a central role in work behavior but in every aspect of our daily lives. Unfortunately, the importance of motivation has led to an unwieldy number of theories on the topic, making understanding or advancement difficult. In this chapter, we provide an overview of the basic building blocks of motivation. We examine these building blocks in relation to different phases of goal pursuit. Integrating work from neuroscience and general psychology, we propose that there are three major goal phases: Goal Choice, Goal Planning, and Goal Striving. The resulting framework we call the Goal Phase System (GPS). Using this framework, we show how motivation unfolds differentially across each stage. The GPS provides an integrated account of motivation over time that can provide clarity to conflicting findings in motivation. After integration, we review how most self-regulatory or motivational interventions can be understood as modifying specific elements of the motivational process during discrete goal phases.
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PART II
Motivational Perspectives
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The Building Blocks of Motivation:
Goal Phase System
Piers Steel and Justin M. Weinhardt
INTRODUCTION
Motivation all begins with a goal. To be moti-
vated, we need some cognitive representation of
an end-state, of what is to be acquired, conducted,
or achieved (Austin & Vancouver, 1996). We may
or may not be aware of this goal, it may be explicit
or implicit, but without it, our behavior is effec-
tively random. Motivation gives directions to our
actions. While this direction is inherently impor-
tant, a series of researchers make the case that
motivation will become among the most impor-
tant fields of social science.
As civilization advances and prosperity
becomes widespread, we remove most of the
external maladies that were once major contribu-
tors to our misery (e.g., predation, starvation, lack
of shelter). By default, the source of our failures
increasingly becomes ourselves. Ainslie (2005)
argues, ‘We smoke, eat and drink to excess, and
become addicted to drugs, gambling, credit card
abuse, destructive emotional relationships, and
simple procrastination, usually while attempting
not to do so’ (p. 635). Stanovich (1999) believes
it is even worse; we exist in an increasingly artifi-
cial or built environment that has sporadic overlap
with the environment of evolutionary adaption.
Since, our motivational impulses are fine-tuned to
the latter, rather than the former, we increasingly
find ourselves motivationally adrift, knowing what
to do but not being motivated to do it. Steel (2010)
stresses that this built environment is not necessar-
ily motivationally neutral; free market capitalism
ensures it is constructed with considerable design,
including features that can coax maladaptive
behaviors, particularly overconsumption. Putting
candy and lottery tickets by the checkout counter
is an example of an insidious but common moti-
vational praxis. Building on this line of reasoning,
Heath (2014) makes an extended case that this will
likely get worse, that ‘absent conscious guidance,
cultural evolution will produce an environment
that is more hostile to human rationality’ (p.146).
We need conscious, rational guidance, which must
be based on a firm understanding of our motiva-
tional foundation.
Unfortunately, the importance of motivation has
led to an unwieldy number of theories on the topic,
making understanding or advancement difficult.
For example, Zeidner, Boekaerts, and Pintrich
(2000) note, ‘the fragmentation and disparate,
but overlapping, lines of research within the self-
regulation domain have made any attempt at fur-
thering our knowledge an arduous task’ (p.753).
Diefendorff and Lord (2008) express a similar
sentiment, ‘the most important future develop-
ment in self-regulation research will involve inte-
grating these approaches so as to develop a more
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comprehensive understanding of goal-directed
behavior’ (p. 163). Locke and Latham (2004),
writing about the future of motivational research,
conclude, ‘there is now an urgent need to tie these
theories and processes together into an overall
model’ (p. 389). And Schmidt, Beck, and Gillespie
(2013), while acknowledging these previous calls,
argued that integration has become ‘all the more
important as the motivational sciences continue to
mature and expand their focus’ (p. 332).
Despite the observed complexity of behavior,
the unity we are seeking as a field could be obtained
because the underlying major motivational mecha-
nisms are relatively simple and few in number.
Chaos theory has shown that considerable system
complexity can arise from rather simple underpin-
nings (e.g., Mandelbrot sets). Following up on this
insight, Navarro and Arrieta (2010), while exam-
ining the chaotic nature of work motivation, con-
cluded that ‘no more than three or four variables
would be required to explain the dynamics of work
motivation’ (p. 253) and argued that ‘mega-models
of motivation developed from a large number of
potential explanatory variables’ were misguided.
While three variables might be an overly
aggressive reduction, our goal here is not to cata-
log all previous motivational perspectives, but to
focus on these likely motivational fundamentals.
We start with temporal motivation theory (Steel &
König, 2006), a meta-theory that Anderson (2007)
concludes, ‘may prove successful … in reducing
the number of theories into a smaller subset of
theories that have a wider range of applicability’
(p. 763). We then extend this with an examina-
tion of goal phases, which indicates fundamen-
tally different formulations may be required to
map different steps in the motivational process
(Gollwitzer, 1990). Finally, we incorporate cyber-
netic or control theories of motivation, particularly
the computational theory of multiple-goal pur-
suit (Vancouver, Weinhardt, & Schmidt, 2010a;
Vancouver, Weinhardt, & Vigo, 2014), which was
specifically designed ‘to continue the process of
integrating motivational theories’ (Vancouver
etal., 2010a, p. 1002). We consider each in turn as
well as combine them, calling the resulting frame-
work the Goal Phase System (GPS). It has three
major phases (Goal Choice, Goal Planning, and
Goal Striving), in which motivation unfolds dif-
ferentially across each stage. After integration, we
review how most self-regulatory or motivational
interventions can be understood as modifying spe-
cific elements of the motivational process during
discrete goal phases.
Temporal Motivation Theory
One strategy for determining what is fundamental
to motivation is simply to identify repeating themes.
If multiple investigations all agree, this is an excel-
lent example of consilience (Wilson, 1998), a
strong form of scientific proof. This was Steel and
König’s (2006) approach for developing temporal
motivation theory, a meta-theory explicitly designed
to integrate the key features of other motivational
formulations. Examining hyperbolic discounting
and the matching law (with origins from behavior-
ism), expectancy × value formulations (with origins
from economics), cumulative prospect theory (with
origins in psychophysics), and need theory (with
origins in personality research), they found several
reoccurring features (Hodgkinson & Healey, 2008).
In all, Steel and König argue, ‘motivation can be
understood by the effects of expectancy and value,
weakened by delay, with differences for rewards
and losses’ (p. 897). The strengths of these four
motivational elements are influenced by both the
individual and the situation and can be examined
from both perspectives (e.g., personality versus
behaviorism). Aside from integration, a benefit of
temporal motivation theory was to explicitly incor-
porate time into our motivational models, some-
thing that field had been lacking (e.g., Fried &
Slowik, 2004; Locke & Latham, 2004; Mitchell &
James, 2001; Schmidt etal., 2013).
When one is evaluating among multiple possi-
ble outcomes for an act, some positive and others
negative, a more complete but also more complex
version of temporal motivation theory is recom-
mended, which Steel and König (2006) cover in
detail. For the purposes of this review, we will
focus on the simplified form of temporal motiva-
tion theory, where people are evaluating one possi-
ble outcome instead a multiplicity of them, which
consequently does not require modeling different
functions for losses versus gains. Before consider-
ing each component in detail, we briefly review
the overall equation:
Motivation = Expectancy × Value
1 + Impulsiveness × Delay
Expectancy occurs in each theory, except in some
forms of matching law (e.g., Mazur, 1987). It rep-
resents the perceived probability that an outcome
will occur, dependent on both the situation and
individual differences. Trait pessimism and chal-
lenging obstacles, for instance, will drive expec-
tancy downwards. Value occurs in all theoretical
formulations and reflects the attractiveness of an
event. Like expectancy, this is influenced by both
the situation and individual differences. The
denominator of the equation captures time or
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temporal discounting, which appears in hyperbolic
discounting, behaviorism, and need theory (i.e.,
press). Being on the bottom of the equation, as their
combined effects increase, motivation decreases.
There are three components of temporal discount-
ing. The first is Impulsiveness, which refers to three
or four different types of individual differences
(Sharma, Markon, & Clark, 2014; Whiteside &
Lynam, 2001) but here focuses on the urgency or
people’s sensitivity to delay use of the term (Cyders
& Smith, 2008). The second is the Delay itself,
which represents the perceived nearness or time
required to realize an outcome. The third is the
constant ‘1’. This defines the upper limit of tempo-
ral discounting as it prevents motivation becoming
infinite when delay is effectively zero.
As Lord, Diefendorff, Schmidt, and Hall (2010)
note, temporal motivation theory has been particu-
larly useful for modeling ‘the motivating power of
approaching deadlines’ (p. 550), especially procras-
tination. This partly reflects that the development
of the theory drew upon a meta-analytic review of
procrastination (Steel, 2007), and we use this self-
regulatory failure here to demonstrate the theory’s
workings. Temporal motivation theory is atypical in
that it does not assume work motivation is always
optimal but at times can operate dysfunctionally.
Procrastination is inherently irrational, defined
as the voluntary delay of an intended course of
action despite expecting to be worse off for the
delay (Klingsieck, 2013) that is putting off despite
expecting to be worse off. Despite numerous theo-
ries of perfectionism or anxiety being the primary
source of irrational delay (e.g., Pychyl & Flett,
2012), it is largely a function of temporal discount-
ing. Gustavson, Miyake, Hewitt, and Friedman
(2014), using twin research to test temporal moti-
vation theory, found 100% of the genotypic vari-
ance in procrastination was due to impulsiveness.
Procrastination arises when we make long-term
plans of actions, such as to start saving or writ-
ing next month. Since all possible actions for next
month are similarly discounted, delay has mini-
mal impact and goal choices are primarily made
by expectancy and value. However, when next
month becomes today, civilization has furnished
us with an impressive array of temptations that are
immediately available. If the intended task can be
delayed, a slippery deadline, or if the firm end date
is still far in the future, a hard deadline, the choice
often becomes between one minimal discounted
temptation and one significantly discounted task.
It is often not until the final hour, when both temp-
tation and task have imminent consequences, that
we become fully engaged with the work at hand.
Having given an overview of temporal moti-
vation theory, we proceed to examine each com-
ponent: Expectancy, Value, Impulsiveness, and
Approach versus Avoidance Orientation. In keep-
ing with an integrative focus, we review each of
these constructs broadly, where the plurality of
variation across each component is considered
minor or superficial.
Expectancy
Expectancies refer to beliefs about contingencies
and are often described as the belief of some out-
come occurring based on some action (Lewin,
1951; Tolman, 1938). Regarding work motivation,
expectancy played an essential role in early theo-
ries (Vroom, 1964) and still plays an indispensa-
ble part in current theories of work motivation
(e.g., Vancouver etal., 2010a, 2014). However, the
nature and definition of expectancy has expanded
over the years (Vancouver etal., 2013).
Self-efficacy is the most prominent concept in
the expectancy family. Bandura (1977) proposes
that self-efficacy is different from expectancy
because the focus is on the belief in the capacity
to achieve a goal and if they do not have a high
belief in their capacity, they will be less likely
to achieve their goal. Other concepts related to
expectancy include confidence/overconfidence
(Dunning, Heath, & Suls, 2004; Moore & Healy,
2008), optimism (Carver & Scheier, 1998), opti-
mism bias (Sharot, 2011) and risk seeking or aver-
sion (Zuckerman, 2007).
The field of work motivation has largely over-
looked the multitude of concepts relating to expec-
tancy. We propose that in most situations, these
terms can be used interchangeably, often are (e.g.,
Locke & Latham, 2002), and can then be largely
considered old wine with new labels (Kirsch,
1985; Vancouver et al., 2013). In addition, there
has been a large discussion about the validity
of the concept and its relationship with motiva-
tion (Bandura, 2012; Bandura & Locke, 2003;
Sitzmann & Yeo, 2013; Vancouver, 2005, 2012;
Yeo & Neal, 2013). We propose that how and when
expectancy is measured matters more than what it
is called (Yeo & Neal, 2006). As we will explore
in more detail during subsequent sections, expec-
tancy (and related concepts) have different effects
depending on the goal phase (Vancouver, More,
& Yoder, 2008). Generally, expectancy has a posi-
tive effect on goal choice, a negative effect on goal
planning, and a mixed effect on goal striving.
Value
It is actually surprisingly difficult to define in a
meaningful way what we value, has utility, find
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rewarding, or reinforcing. For example, Rescher
(1982) summarized philosophical and social sci-
ence’s considerable attempts at providing a coher-
ent definition of value as having ‘failed’ (p. 1).
The difficulty lies in that value, utility, rewards,
and reinforcers are essentially unseen entities that
are inferred to exist from the effects they produce.
Consequently, despite inauspicious beginnings in
a nursing journal, the adage ‘what gets rewarded,
gets done’ (Holle & Armocida, 1988, p. 84) con-
cisely captures our understanding. It is essentially
identical to the behaviorists’ position, who define
positive reinforcers as stimuli ‘that increase the
likelihood of the behavior that precedes them’
(Schwartz, 1989; p. 28). Or consider economists.
Hodgson (2011), when reviewing economic util-
ity, notes that the concept fits everything yet
explains nothing as it is an unobservable ‘blank
cheque’ that can explain any behavior without fear
of refutation. It is circular logic if we explain our
behavior by unseen preferences and then identify
these same preferences by what is revealed by our
behavior. Attempts to escape this tautology do not
get far beyond the prison walls, such as Cameron
and Pierce’s (1994) definition: ‘Rewards are stim-
uli that are assumed to be positive events, but they
have not been shown to strengthen behavior’
(p. 364). Actually, we cannot even go so far as
this, stating that rewards are positive events.
Addiction research has demonstrated we can want
what we do not like, and feel compelled to pursue
paths that bring us pain (Berridge, 2009).
Presently, rewards can only be definitively
revealed by seeing their effects on people’s
actions, or as economists like to say ‘De gustibus
non est disputandum’ (Stigler & Becker, 1977, p.
76): There is no accounting for tastes.
Despite the challenge that preferences may
need to be revealed, the construct of value is indis-
pensable. All motivational theories contain a form
of value, for without it there can be no ‘why’.
The dominant framework for understanding the
issue of why has been to break down rewards into
intrinsic and extrinsic, with Reiss (2004) tracing
this division back to Aristotle and his discussion
of motives as being intrinsically an end, done
for its own sake, or extrinsically a means, done
instrumentally to enable some other outcome.
Consequently, we are extrinsically motivated
when we are driven by the outcome of our actions,
such as for financial incentives, but intrinsically
motivated when driven by rewards inherent in
the task itself (Ryan & Deci, 2000). The differ-
ence between the two is not always readily appar-
ent. For example, is raising a fork to one’s mouth
instrumental, so one can experience the pleasure
of eating, or intrinsic, part of the process of eating?
Given this ambiguity, Thierry (1990) concluding
that ‘the intrinsic–extrinsic motivation distinction
is based on a delusion’ (p. 80); all rewards, both
intrinsic and extrinsic, create an internal state of
motivation spurred by some external stimuli.
Despite the tension between the two, the distinc-
tion of intrinsic–extrinsic has proven useful. We
use rewards to incent a wide variety of behaviors
and there can be conflict among types of rewards.
It is possible that incentives we purposefully tack
on to tasks interfere with those already naturally
or intrinsically occurring. Cerasoli, Nicklin, and
Ford (2014) conducted a meta-analysis that exam-
ined the interrelationship among intrinsic motiva-
tion, incentives and performance. In addition, they
examined two key moderators, how performance
was measured (quantity versus quality) and the
salience of the incentives (directly tied to per-
formance versus indirectly tied to performance).
Cerasoli et al. found that intrinsic motivation is
positively related to performance across a number
of domains (e.g., work, school and physical activ-
ity). They also found that when incentives were
added in combination with intrinsic motivation,
this actually increased performance. However,
when incentives were directly tied to performance,
the effect of intrinsic motivation was depressed.
On the other hand, when incentives were indi-
rectly tied to performance, intrinsic motivation
was an important determinant predictor of perfor-
mance. This result supports motivational crowding
(Frey & Jegen, 2001), where extrinsic incentives
can overshadow other motivational forces. Finally,
intrinsic motivation was a better predictor of qual-
ity performance, whereas incentives were a bet-
ter predictor of quantity performance. This work
shows that incentives and intrinsic motivation can
work together in concert to increase motivation and
performance, but researchers and managers need
to be careful about what type of performance they
want (quantity or quality) and how the incentives
are tied to performance (directly or indirectly).
While motivation researchers know that there
is much more to rewards than financial incentives,
more could be done here. Personality and need
theory has done the most work on explicating what
type of rewards are associated with individual
profiles, beginning with Murray’s (1938) catalog
of needs. Presently, a large part of personality’s
predictive power comes from assessing three
profiles of needs – need for affiliation, need for
achievement, need for power – and using them to
suggest other behavior of this kind is likely forth-
coming (Winter, 1996). For example, DeShon
and Gillespie (2005) consider fundamental cat-
egories of goal content, the ‘why’ of motivation,
are to address: agency (power), esteem (achieve-
ment), and affiliation. Still, these three primary
needs are not an exhaustive list and there are other
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well-supported taxonomies to consider, such as
Haidt’s (2012) six elements comprising Moral
Foundation Theory or Reiss’ (2004) argument
for sixteen primary motives. For example, ethics,
respect, or justice are important for job seekers
selecting organizations (Ogunfowora, 2014) and
are unlikely to be fully subsumed under needs for
affiliation, achievement, and power. What is the
exact relationship between behavior and the type
of rewards? To what degree is one type of motiva-
tor fungible with that of another? By expanding
our motivational palette, we should be rewarded
with improved prediction and understanding of
behavior.
Impulsiveness
Over two millennia ago, Buddha likened his mind
to a wild elephant, full of lust and desire but kept
in check by a trainer (Haidt, 2006). Plato describes
our motivations as a chariot being pulled by two
horses, one of reason, well-bred and behaved, and
the other of brute passion, ill-bred and reckless. At
times, the horses pull together and at other times,
they pull apart. Adam Smith, in his book The
Theory of Moral Sentiments, wrote about the bal-
ance between ‘the passions’ and ‘the impartial
spectator’, where the latter tries to moderate the
former’s excesses of lust, hunger, and anger
(Ashraf, Camerer, & Loewenstein, 2005). And
Sigmund Freud continued Plato’s equestrian anal-
ogy by comparing us to a horse and rider. The
horse is desire and drive personified, powerful but
needs direction from the rider, who represents
reason and commonsense. This division has been
rediscovered by dozens of other investigators,
each with their own angle, emphasis, and termi-
nology for the same multiple self. Along with 25
other terms documented by Stanovich (2011),
these include: emotions versus reason, habit
versus planned, experiential versus rational, hot
versus cold, affective versus deliberative, impul-
sive versus reflective, viscerogenic versus psycho-
genic, Dionysian versus Apollonian, intuitive
versus reasoning (Baumeister, 2005; Bechara,
2005; Bernheim & Rangel, 2002; Redish, Jensen,
& Johnson, 2008). Among all these possibilities to
describe this split, and evidently drawing on the
full poetry of science, the blandest seems to have
gained the most traction: System 1 versus System
2 (Kahneman, 2003).
More recently, with the onset of fMRI studies
that reveal thinking in situ, we come to see this
duet as less of an analogy and more of an accu-
rate account of how we make decisions. Falling
under the larger rubric of dual process theories
(Evans, 2008), the interplay between System
1 and System 2 is reflected in our brain’s very
architecture. System 1 or the limbic system (more
specifically, the mesolimbic dopaminergic system
such as the amygdala and nucleus accumbens) is
the evolutionarily older, and its purview is often
the here-and-now. It is aroused by sensations of
sight, smell, sound, or touch that usually indicates
‘immediately available’, and this often results in
heightened cravings and impulsiveness. System 2
is part of the neocortex or ‘new bark’, specifically
the prefrontal cortex, and it enables long-term
plans and consideration of our extended future. As
might be expected by the numerous analogies to
a rider atop some great beast, System 1 with its
direct line to amygdala (the source of our strong
emotions) tends to be stronger and quicker than
System 2. Gifford (2002) provides a particularly
good account of the resulting conflict (p. 129):
It is this divergence between the cultural and bio-
logical rates of time preference that creates a
potential internal nature versus nurture conflict
leading to self-control problems [like procrastina-
tion]. The higher level prefrontal working memory
system allows the agent to consider possible
events in the extended future and to discount
those events at a rate appropriate to the individu-
al’s current environment. The lower level [limbic
system] does not have access to events not yet
experienced, and as a result, ignores these purely
abstract events; it also incorporates the high level
discount rate similar to that used by non-human
primates and some other mammals that is a prod-
uct of natural selection.
In the parliament of the mind, it is common for the
carefully laid plans of the prefrontal cortex to be
trumped by the cravings from the limbic system
(Bechara, 2005).
When our decision-making becomes limbic
heavy or prefrontal light, we refer to this as impul-
sive. Reflecting impulsiveness’ System 1 versus
System 2 roots, Sharma et al. (2014) show that
this dichotomy shows up at a measurement level.
Some impulsiveness assessments, such as the
Premeditation scale of the UPPS (Whiteside &
Lynam, 2001), focus on the limbic Disinhibition
vs. Constraint aspect. Other measures, such as
the UPPS’ Perseverance scale, focus on prefron-
tal Will vs. Resourcelessness. The work motiva-
tional field studies impulsiveness, though often
divorced from the biological underpinnings and
unintegrated with parallel programs conducted
under different terms. For example, Schouwenburg
(2004) notes that ‘Various studies show a very dis-
tinct clustering of related traits: trait procrastina-
tion, weak impulse control, lack of persistence,
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lack of work discipline, lack of time manage-
ment skill, and the inability to work methodically’
(p. 8). Given the number of work studies that include
conscientiousness, where many of these impulsive
related facets are nested, particularly those Will vs.
Resourcelessness-related, the field has indeed been
researching this, but predominantly as personality
traits, which tend to be atheoretical or descriptive.
Their connection to the process or explanatory
model of the limbic-prefrontal cortex duet is often
overlooked, which if attended to would consider-
ably reduce construct proliferation.
Approach vs. Avoidance
Whereas motivation can be divided by whether it
is System 1 or System 2, it can also be divided by
gains and losses or approach and avoidance. Are
we seeking an outcome or seeking to avoid it?
During the construction of temporal motivation
theory, this feature was found in prospect theory
and need theory, but it was extremely well sup-
ported by a variety of other investigations (Carver,
Sutton, & Scheier, 2000; Elliot & Thrash, 2002;
Higgins, 1997; Ito & Cacioppo, 1999). Since this
time, support has further deepened to justify a
dedicated handbook on the topic (Elliot, 2008),
who in a historical review also makes an excellent
case that this distinction is ‘one of the oldest ideas
in the history of psychological thinking about
organisms’ (Eilliot, 2006, p. 111).
At its basic level, the approach versus avoid-
ance dichotomy means that expectancy, value,
and impulsiveness will be modified depending
upon whether we have framed our goal as some-
thing to be lost rather than something to be gained.
We will become more risk prone or risk averse,
more avaricious or less, and more impulsive or
more patient. The appearance of an approach or
avoidance mindset is influenced both by person-
ality traits (e.g., attributional style, Weiner, 1991)
and by what the task itself evokes (e.g., when
mistakes are very costly, we might find ourselves
focusing on avoiding them). Temporal motivation
theory unevenly reviews how expectancy, value,
and impulsiveness change with an approach ver-
sus avoidance mindset. Consistent with prospect
theory, a threat of a loss is considered more moti-
vating that the equivalent gain. Consequently, the
frame with which we make decisions matters, as
we tend to be risk seeking to avoid a loss but risk
avoidant to keep a gain.
Behavioral economics and neuroeconomics,
which have blossomed over recent years, provides
support and refinement. On average, we tend to
adopt the avoidant mindset and tend to be risk
averse. For example, two large natural field exper-
iments are especially evocative. Using data from
the internationally successful television shows,
Who Wants to be a Millionaire? and Deal or No
Deal (Hartley, Lanot, & Walker, 2013), contes-
tants show a degree of risk aversion on average.
When, for example, posed between two equally
likely outcomes, one of possibly loosing $99,999
but at the chance of gaining $400,000 more, con-
testants are more likely to choose the ‘sure thing’,
and stick with their present winnings of $100,000,
despite this being inferior in terms of expected
value. Supporting this finding is the neurobiol-
ogy of decision-making, sometimes termed neu-
roeconomics, which is proceeding at a rapid pace
and ‘aiming to develop a unified theory of value
and choice’ (Levy & Glimcher, 2012, p. 1036).
Their goal is determined exactly the way our brain
processes subjective value, with intense focus
on elements of prospect theory. While decisions
among choices have shown eventually to convert
to a ‘common neural currency’, there is evidence
that neural components are activated differentially
depending on whether it is a loss or a gain (Brooks
& Berns, 2013).
The approach versus avoidance dichotomy
extends to impulsiveness as well, though Steel and
König (2006) leave the matter somewhat open:
‘Differences between positive and negative impul-
siveness have not yet been definitively established,
although they do appear to differ’ (p. 898). Since
this time, some consensus has emerged. Cyders
and Smith (2008) review the issue under the term
‘urgency’. Drawing from a variety of research
streams, they concur that impulsiveness can be
further split into two components, depending on
whether the emotional state reflects positive affect
or negative affect. In addition, Mogilner, Aaker and
Pennington (2007) show evidence from consumer
research that avoidance goals are discounted more
steeply, meaning that rewards may be most moti-
vating ahead of time while costs become more
salient in the short term. Notably, this is almost
the exactly same conclusion that Dollard and
Miller (1950) drew over a half-century earlier:
‘The strength of avoidance increases more rapidly
with nearness than does that of approach. In other
words, the gradient of avoidance is steeper than
that of approach’ (p. 352).
Goal Phase System (GPS)
New Year’s resolutions are one of the better of our
modern day institutions. Originally, a holiday to
celebrate Janus the two-faced god (i.e., January is
where we have an opportunity to look forward and
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The Building Blocks of MoTivaTion: goal Phase sysTeM 75
back over the past year and on to the new),
Christians tried to reform the holiday into a holy
day by making it a time to redouble one’s commit-
ment to God through religious resolutions.
Presently, we get the benefit of both, where after a
New Year’s party many make a resolution to live
their lives, in some way, for the better. In a pair of
papers, Norcross and colleagues determined how
successful were these resolutions (Norcross,
Mrykalo, & Blagys, 2002; Norcross, Ratzin, &
Payne, 1989). As our experiences might confirm,
there was massive drop off, with only 71% hold-
ing true by week two, 64% by February, and only
50% by April. This drop off is not uncommon and
is more broadly studied as intention-action gaps or
preference reversals (Frederick, Loewenstein, &
O’Donoghue, 2002; Green & Myerson, 2004).
Despite making a clear choice or intention to act,
the action does not follow because we fail to
pursue effectively our earlier goals despite cir-
cumstance remaining ostensibly the same. Clearly,
the forces that help determine what goal we chose
were not operating to the same degree or perhaps
even in the same way later on. There must be
multiple goal stages.
As Diefendorff and Lord (2008) note, there
have also been multiple phase theories, stretch-
ing back to Lewin, Dumbo, Festinger, and Sears
(1944). Lewin et al. posit two stages: goal set-
ting, where we deliberate, wish, or establish what
goals we will pursue, and then goal striving, dur-
ing which we pursue them. Over the subsequent
decades, every model contains at least these two,
and at times nothing more. For example, regula-
tory mode theory divides action orientation into
an assessment mode and a locomotion or doing
mode (Kruglanski etal., 2000). In her review of
motivation, Kanfer (2012) also settles on essen-
tially the same two: goal choice and goal pursuit
or striving. During goal choice, we decide which
goal we will allocate resources. During goal striv-
ing, we attempt to implement steps that will help
realize our goal. Importantly, we may fail in our
attempts here, and though we are doing nothing
that advances our goal, we are in this second stage.
Will two goal stages suffice? To develop GPS,
we want to identify goal phases where there are
fundamentally different principals operating.
Gollwitzer (1990), whose work in this area has
become among the most influential, recognized
the importance of goal choice and goal striving,
but argued for two more. Drawing on Heckausen
and Gollwitzer’s (1987) ‘Rubicon Model’ of
action phases (named to stress little overlap
between stages), he posits these two additional
phases: goal planning and goal evaluation. During
goal planning, we decide on the when, where,
and how we will act. During goal evaluation, we
reflect on the degree we have achieved the desired
goal, and whether we should continue to pursue it.
Importantly, each phase is associated with a differ-
ent mindset, which is potentially a way of evaluat-
ing motivationally relevant criteria.
This four-stage model (i.e., choice, planning,
striving, evaluation) has waned somewhat over
the years, with the differences between when
goal evaluation stops and goal choice begins once
again becoming unclear (Diefendorff & Lord,
2008). Functionally, we must evaluate our goals
to know we have completed them, but the moti-
vational properties appear to be largely the same
as goal choice. Gollwitzer (2012), for example,
now almost exclusively focuses on the qualita-
tive differences between a deliberative mindset,
associated with goal choice, and an implemental
mindset, associated with goal striving, effectively
reverting to the two-stage model. The status of
goal planning, however, remains less certain.
Neuroscience is proving helpful to understand
how goal phases work and their number. Whatever
theories we develop, they can be informed by
what we know about our brain’s mechanisms.
Consequently, Diefendorff and Lord (2008)
applied neuroscience to help explicate how the
separate goal phases operate. O’Reilly, Hazy,
Mollick, Mackie, and Herd (2014) extend this
strategy by using neuroscience to establish the
very number of goal phases. Relying on expert
knowledge of brain areas and their workings, they
develop a detailed computational model of goal
behavior. Their conclusion is familiar, that there
are two primary phases: goal selection and goal
engagement. Crucially, ‘these states have strongly
dissociable properties’ (p. 4), meaning that they
must be modeled separately. However, they also
suggest that goal planning might be a third stage
that is ‘episodically-driven reevaluation and
planning of future goal states’ (p. 37). Similarly,
Stanovich (2011) makes an extended argument for
a tripartite model that includes a separate ‘reflec-
tive’ mind, which is supervisory in nature, involv-
ing simulating possible futures and determining
goal priority and regulation. Finally, Vancouver
and colleagues (2008) have broken down motiva-
tion into choice, planning, and striving, finding
that expectancy and value demonstrate disparate
findings across the motivational process.
Based on this work, GPS incorporates a three-
stage framework of motivation: goal choice,
goal planning, and goal striving. As simplified in
Figure 4.1, the typical effect of the building blocks
of motivation often switches as one passes through
these three stages. Though inevitably some goal
choice initiates the phases, the exact order of them
is not consistently in sequence. During a larger
and longer-term goal, especially with multiple
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sub-goals, each of these stages may be revisited
many times (Bateman & Barry, 2012). For exam-
ple, goal abandonment comes from revisiting goal
choice and finding insufficient commitment. From
a goal phase perspective, there can be no single
theory of motivation. As Norem (2012) states,
during her own integration of person and situa-
tion motivational perspectives, ‘Attempts to stay
within the historical or prototypical boundaries
of personality and social psychology are doomed
to either distort those boundaries beyond recogni-
tion, or to ignore significant and arguably relevant
material’ (p. 287). Consequently, we consider how
motivation manifests during each of GPS’ three
goal phases.
Motivation during Goal Choice
Right from the beginning, Lewin et al. (1944)
thought that expectancy × value models do best
here, during goal choice. At this initial stage of
GPS, we have a deliberative mindset, focusing on
the feasibility (i.e., expectancy) and desirability
(i.e., value), in a largely rational and therefore
multiplicative manner (Gollwitzer, 1990).
Inevitably, this is where expectancy × value theo-
ries and their derivations, such as Vroom’s VIE
theory or Fishbein and Ajzen’s (1975) theory of
reasoned action, should show their highest verisi-
militude. There is little disagreement on this point.
Economists, management researchers, philoso-
phers, and psychologists concur that expectancy
and value are strongly and positively related to
goal choice (Anand, 1993; Bandura, 1997; Carver
& Scheier, 1998; Edwards, 1954; Locke &
Latham, 2004; Ramsey, 1931; Vancouver, 2008;
Vroom, 1964). If an individual is faced with a
decision between choosing between goals, they
are more likely to choose the option that gives
them more of what they want and with a higher
probability of doing so.
Though expectancy × value theories are favored
during goal choice, they are not perfect. This class
of motivational theories are often considered repre-
sentative of the rational model and given the copi-
ous critiques of classical economics (e.g., Camerer
& Fehr, 2006; Hodgson, 2011; Kahneman, 2003),
which has doubled-down on rationality, it is impor-
tant not to overreach. At all times, we are best
described as quasi-rational (Thaler, 1994), mean-
ing there are several caveats. Many of these devia-
tions from the rational model can be understood as
motivational ‘hardwiring’ for our ancestral hunting
grounds, heuristics that had previously maximized
our chances of reproductive success.
For value, Tversky and Kahneman’s (1992)
cumulative prospect theory adds another level of
refinement, demonstrating that there is not a one-
to-one correspondence between perceived and
actual rewards and probabilities. Consistent with
the psychophysics of ‘Just Noticeable Differences’,
we tend to require exponentially more of a reward
to feel more rewarded. Also, a potential loss of a
reward is more motivating than the potential gain
of the same amount. As discussed, we have a moti-
vational system fine-tuned for an environment
of evolutionary adaption, which we no longer
inhabit. Exponential discounting of rewards, for
instance, is adaptive if such rewards (e.g., food)
can be consumed only at a limit rate and the excess
will effectively disappear (e.g., spoil).
For expectancy, we tend to overestimate for
ourselves the likelihood of positive events and
underestimate the likelihood of negative events,
an optimism bias (Sharot, 2011). Despite disap-
pointing base rates, we get married, start busi-
nesses, and adopt risky lifestyles. When asked
about our chances of divorce, bankruptcy, or
• Expectancy (+)
• Value (+)
• Impulsiveness (+)
Goal Choice
• Expectancy (–)
• Value (+)
• Impulsiveness (–)
Goal Planning •Expectancy (+/–)
• Value (+/–)
• Impulsiveness (–)
Goal Striving
Figure 4.1 Goal Phase System
Note.
The approximate effect of the building blocks of motivation during each goal phase
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The Building Blocks of MoTivaTion: goal Phase sysTeM 77
health complications, we underestimate. McKay
and Dennett (2009) discuss how these optimis-
tic judgments could have evolved. Going further,
Johnson and Fowler (2011) created a simulation
of the role inflated expectancy beliefs could have
in survival. Summarizing these positions, in evolu-
tionary environments where rewards were highly
valuable compared to the costs of fighting for
those resources, it is advantageous to have inflated
expectancy beliefs to obtain the resources because
veridical beliefs may be self-limiting. Without
the inflated beliefs, we would not go out for risky
hunts, travel to new regions, or try new food
resources. In today’s environment, this optimism
bias is not as warranted but not necessarily unben-
eficial. As Sharot (2011) concludes, while ‘classic
theories in economics and psychology assert that
correct beliefs will maximize reward and mini-
mize loss, many sources of evidence point to the
conclusion that optimism is nonetheless advanta-
geous compared to unbiased predictions’ (p. 944).
Returning to Johnson and Fowler (2011), inflated
beliefs can still be handy when there is competi-
tion over contested resources. For example, in
academia, the top journals in our field all have an
acceptance rate of 5% or lower. However, submis-
sions to the journals for the year are often above
1,000. This high submission rate appears inconsis-
tent with the low acceptance rate, so to submit to
the top journals we need to have somewhat inflated
expectancy beliefs. On the other hand, those with
realistic expectations never submit and invariably
never publish there, so irrational exuberance can
be adaptive. The same logic plays out in many dif-
ferent situations.
Then, there is the issue of time. While expec-
tancy × value models rarely include time as a vari-
able (Locke & Latham 2004; Sonnentag, 2012),
when they do, they typically incorporate an expo-
nential function and a low discount rate, which
is consistent with the rational model. However,
hyperbolic discounting, as used in temporal moti-
vation theory, and a high discount rate tends to
fit our choices better, reflecting the evolutionary
kluge composed of the limbic and the prefron-
tal cortex (Fudenberg & Levine, 2006; Inbinder,
2006). As Marcus (2008) writes, ‘over hundreds of
millions of years, evolution selected strongly for
creatures that lived largely in the moment’ (p. 84).
Fortunately, poor modeling of time is not always
an issue regarding goal choice. When deciding
between outcomes that are in the same relative
time envelope or horizon, the effect of impul-
siveness or delay becomes a constant and can
be dropped from the equation. It is important to
stress that it is not the absolute difference in time
between the choices that are attended to but the
relative difference. The difference in motivation
between two choices, one that can be realized in a
year and another in a year and a day, is fractional.
On the other hand, the motivational differences
between two options, one that can be realized
immediately and another only tomorrow, are mon-
umental. It is in the latter of these two cases that
we need to incorporate temporal discounting (e.g.,
temporal motivation theory), or find our models
poorly predict.
Finally, as Scholer and Higgins (2011) review,
expectancy and value are affected by approach
and avoidance. When people are promotion
or approach focused, they more closely fit the
rational model, seeking to maximize outcomes.
However, when prevention or avoidance focused,
they become remarkably less sensitive to expec-
tancy. For example, the odds of an American citi-
zen dying by a terrorist event, foreign or domestic
in origin, in the last years are approximately 1 in
20 million. As per the National Safety Council’s
data, this can be compared with more prosaic
deaths, such as dying in a car accident (1 in
18,585) or accidentally suffocating yourself in bed
(1 in 807,349). With over $1 trillion dollars spent
in anti-terrorist security in ten years (not including
the cost of military actions and conflicts), this type
of expenditure is perverse, estimated at roughly
$400 million per life saved (Mueller & Stewart,
2011).
Aside from these four general trends, the
judgment and decision-making field has a lit-
any of other biases or ‘anomalies’ that affect
how we estimate expectancy and value during
goal choice. Bazerman and Moore (2013) orga-
nize these around three heuristics: Availability,
Representativeness, and Confirmation. For exam-
ple, one aspect of the Representative heuristic is
that we at times judge two events occurring in
sequence as being more likely than either event.
Rather than review all these biases, their purpose
here is simply to stress that expectancy × value
models, which includes temporal motivation the-
ory, are approximations and provide only a rea-
sonable account for most goal choice situations.
Such discrepancy is normal, however, and not nec-
essarily cause for concern, a point that has been
made numerous times, such as Box and Draper’s
(1987) famous ‘all models are wrong, but some
are useful’ (p. 424) or Fox’s (2011) paraphras-
ing of the same point, ‘All scientific models are
oversimplifications. The important test is whether
they’re useful’ (p. xiv). In most situations, these
‘biases’ can be considered heuristics that con-
serve cognitive resources and enable a close to
optimal choice among many commonly occurring
situations. Consequently, an option is indeed more
likely to be chosen with increases in expectancy
and value or, to some degree, their interaction.
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While additive models will often suffice (Schmitt
& Chan, 1998) since these interactions need ratio
scales (i.e. a meaningful zero point) to be properly
observed (Anderson, 1970), a blatantly impossible
task will still curtail choosing a goal regardless of
the possible reward. Likewise, an option usually
diminishes in luster when we are impulsive and
the longer we must wait for a reward.
Motivation during Goal Planning
The failure of expectancy × value theories to
account for motivation across all goal phases led
to criticism, disillusionment, and disuse, at least
within psychology (Kanfer, 2012). Latham (2011)
chronicles the decline, which culminates with Van
Eerde and Thierry’s (1996) meta-analysis on the
topic. Attempts to test expectancy × value theories
found that the components predicted better when
allowed to operate separately rather than multipli-
catively. Though the sporadic use of ratio scales
contributed (Anderson, 1970), part of the problem
is that of overextension. While expectancy and
value remain important variables throughout the
motivation process, their psychophysics does not
remain constant. Going from goal choice to goal
planning and goal striving in GPS, one passes
through ‘the looking glass’, and one motivational
principle in particular radically changes in direc-
tion: expectancy.
We know that from work on the planning fal-
lacy and the illusory superiority effect that individ-
uals are poor planners (Buehler, Griffin, & Ross,
1994; Kahneman & Lovallo, 1993; Kahneman &
Tversky, 1979). Individuals falsely believe they
will be able to complete a task in a shorter amount
of time than it actually takes to complete the task.
There are numerous examples of the planning fal-
lacy, as it occurs in almost all projects, from the
small to the very large. For example, the Denver
International Airport was delayed 16 months with
costs 300% more than initially planned (Buehler,
Griffin, & Ross, 2002). Our over optimism
regarding the time it takes is often predicated on
a tendency to simplify, failing to incorporate the
inevitable deviations from our imagined ideal set
of events (Roy, Christenfeld, & McKenzie, 2005).
For big planning projects, for example, we fail to
anticipate sick days, shipment delays, or union
problems. Similarly, academics may believe that
they can write an academic paper in three months
because, when they make this plan, they only
think about how long it will take to analyze the
data and write up the project. They neglect to
consider the intense competition for their time,
which comes from service committees, teaching
requirements, family commitments, and graduate
student obligations.
Although, the majority of research and theory
regarding expectancy has focused on the positive
effect, Bandura (1997) proposed that in a prepara-
tory context that self-efficacy might be negatively
related to performance, implying that self-doubt
might be a good thing. For example, when dis-
cussing athletes, Bandura states, ‘In approaching
learning tasks, athletes who perceive themselves
to be highly efficacious in their capabilities have
little incentive to invest much effort in tedious pre-
paratory practice. Some uncertainty clearly ben-
efits preparation’ (p. 405). When individuals have
high self-efficacy, they may not allocate resources
towards preparing for an upcoming task because
they believe their ability will already suffice.
Similarly, Bandura has been cautious about self-
efficacy’s positive role in an educational prepara-
tory context. For example, Bandura (1997) states,
‘Students who greatly underestimate the difficulty
of academic course demands and remain blissfully
free of self-doubt are more likely to party than to
hit the books to master the academic subject mat-
ter’ (p. 76). In a classroom setting, where individ-
uals need to prepare for an upcoming exam, low
rather than high self-efficacy may be preferred.
In line with Bandura (1997), Vancouver (2008)
proposes that expectancy negatively relates to
motivation while planning for a goal they have
already adopted. In that case, the higher individu-
als’ expectancy, the fewer resources they will
allocate towards the goal because they believe it
can be achieved more easily (e.g., requires fewer
resources). For example, once a student starts tak-
ing a course, expectancy will negatively relate to
motivation because when individuals have high
expectancy that they will get an A, they believe
fewer resources (i.e., study time) are needed to get
an A compared to when self-efficacy is lower.
These two processes (i.e., goal choice and
resource allocation) create a complex relationship
between self-efficacy and motivation. Vancouver
(2008) has proposed that the relationship between
expectancy and motivation is non-monotonic and
discontinuous. Figure 4.2 depicts this relationship.
Kukla (1972) first discussed the non-monotonic,
discontinuous relationship and later Carver and
Scheier (1998) integrated it into their self-regu-
lation theory. Generally, this relationship reflects
the notion that when people believe they have no
chance of achieving the goal (i.e., extremely low
expectancy), they are not motivated to strive for
that goal (i.e., allocate no resources) and effec-
tively abandon it. However, if the individual
believes the task is somewhat achievable, but
still difficult, they will plan to allocate consider-
able resources towards realizing it. Likewise, as
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The Building Blocks of MoTivaTion: goal Phase sysTeM 79
perceived ability (i.e., expectancy increases), they
will plan to allocate fewer resources. In the same
way we are often cognitive misers, using the short-
cut of heuristics, we are also motivational misers,
not wanting to spend more energy and time on a
task than necessary. Vancouver etal. (2008) pro-
vided empirical support for this model.
On the other hand, the effect of value and time
prove to be more consistent with goal choice,
operating in the same fundamental manner. While
several theories of motivation predict no effect
for value on planning or resources independent
of goal level (e.g., Klein, 1991; Locke & Latham,
1990; Wright & Brehm, 1989), other theories and
research predict a positive effect (Hyland, 1988;
Pritchard & Curts, 1973; Terborg & Miller, 1978;
Yancey, Humphrey, & Neal, 1992). Recently,
Sun, Vancouver, and Weinhardt (2014) find sup-
port for the latter by examining the discontinuous
non-monotonic model (Figure 4.2) in regards to
value. Across two studies, they found that value
increased planned resource allocation. As can
be seen in Figure 4.2, an increased value will
make the discontinuity happen sooner and more
resources are allocated. Like for goal choice, we
are still quasi-rational regarding how we assess
value. For example, planning is adversely influ-
enced by affective forecasting errors regarding
the capabilities and desires of our future selves
(O’Donoghue & Rabin, 2008; Wilson & Gilbert,
2005).
Where expectancy and value may bias our plan-
ning, impulsiveness can make planning disappear.
Overall, individuals who are high on impulsive-
ness are poor or even absent planners (Sharma
etal., 2014), and they may go straight from goal
choice to goal striving. Instead of thinking about
how to achieve their goal and develop strategies,
they just start working towards the goal (or not).
This can happen even for the less impulsive when
their choices can be immediately realized, such as
for an impulse purchase (Kalla & Arora, 2011),
as there is effectively no time between choice and
implementation. Why does this impulsiveness do
this to us? The explanation will be familiar as it
draws on the previously described limbic system/
prefrontal cortex duet. Specifically, the prefrontal
cortex is the brain region where planning arises.
The more developed the prefrontal cortex, the bet-
ter we are at planning. One of the more poignant
examples of this is our teenagers, who are worse
at planning than adults because they have a less
developed cognitive-control system in the brain
(Steinberg, 2007). The application of this neuro-
science to organizational science is still underde-
veloped itself, though there are advocates (e.g.,
Volk & Köhler, 2012); continued application of
this knowledge base should be pursued.
At its basic level, the approach and avoidance
dichotomy results in modification of expectancy,
value, and impulsiveness. At the planning stage,
this is best discussed in terms of regulatory fit.
Higgins (2005) has extensively researched ‘the
value of fit’, that is the benefits that occur when
regulatory frame is aligned with the type of goal
we are pursuing. Higgins proposes two types of
regulatory frames, a promotion focus where we are
concerned with realizing positive outcomes and a
prevention focus where we are concerned with
avoiding negative outcomes. To maximize their
motivation benefits, in signal detection terms, a
promotion focus should pursue goals constructed
Low
Motivation
High
Expectancy
High value
Low value
Figure 4.2 The relationship between expectancy and value on motivation
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80
around maximizing hits and avoiding misses
while a prevention focus should pursue goals con-
structed around ensuring correct rejections and
avoiding false alarms. For example, a technology
company with a promotion focus would reward
creativity and the generation of new products
while if it had a prevention focus it would empha-
size the quick elimination of unprofitable lines and
extensive justification for any new investment. In
short, promotion leads to eager strategies while
prevention promotes vigilant strategies (Higgins,
1997; Scholer & Higgins, 2011). The nature of
goal influences the planning to achieve it.
In addition, prevention focus leads to quicker
initiation of a goal and those who employ it are
better at maintaining a plan than promotion
focused individuals (Freitas, Liberman, Salovey,
& Higgins, 2002; Poels & Dewitte, 2008; Scholer
& Higgins, 2011). Prevention focused people see
their goals as more important obligations (i.e.,
higher value) than promotion focused individu-
als and consequently allocate more resources to
it. Also, prevention focused individuals may be
better at resisting temptation because they remove
themselves from enticements as well as develop
escape routes that deliver themselves from temp-
tation. For example, an employee may set a goal
of accomplishing some work task by the end of
the day and, to do so, they may temporarily block
their email or access to distracting Internet sites.
Notably, these tactics tend to be used by those who
are themselves pessimistic regarding their ability
to resist temptation, while those more confident
tend to expose themselves to more temptations
and subsequently lose control (Nordgren, van
Harreveld, & van der Pligt, 2009).
Motivation during Goal Striving
Similar to the goal planning, expectancy × value
theories fail to fully translate to the goal striving
phase of GPS. For example, Klinger (1977), in
early work notes, ‘expectancy × value theories
have been only very modestly successful in pre-
dicting vital aspects of goal striving, such as work
and quality of performance’ (pp. 329–330). Where
during goal planning, it was principally just
expectancy that changes form and function, during
goal striving, value, impulsiveness, as well as
approach versus avoidance orientation also need
goal phase specific consideration. Every major
aspect of motivation has different functioning,
emphasis, or impact during goal striving.
After we have chosen a goal, the feasibility and
desirability, that is expectancy and value, tends to
further increase (Gollwitzer, 2012). An example of
this is the endowment effect (Apicella, Azevedo,
Fowler, & Christakis, 2014), where we value our
belongings and acquisitions more after obtaining
them. Given goal evaluation and goal switching
themselves have costs, this can be considered a
useful form of goal shielding (Shah, Friedman, &
Kruglanski, 2002), allowing us to inhibit compet-
ing choices and more fully engage in singular goal
pursuit. However, during goal striving, excessive
expectancy can work against timely goal comple-
tion in multiple ways. During goal choice, we
are indifferent to where the source of our expec-
tancy comes from. If the outcome is sufficiently
likely, and the reward competitively desirably, we
will make pursuit intentions. During goal striv-
ing, the effects of expectancy transform, largely
as described during goal planning. Reminiscent
of Aesop’s Tortoise and the Hare cautionary tale,
those who are confident of the outcome, are less
likely to invest in resources, which manifests itself
here during goal pursuit. We take it easy, perhaps
too easy. Also as mentioned, those overconfident
in their impulse control beliefs tend to expose
themselves to tempting situations, whereupon
the impulse multiples in power and overrides the
long-term goal (Nordgren etal., 2009). Choosing
to work with a television nearby or take ‘a quick
break’ to have a drink with friends are potential
examples. In addition, Polivy and Herman (2002)
describe what they call the false hope syndrome.
Overconfidence regarding the speed, size, or
ease of life changes is actually associated with
lower levels of striving and perseverance. When
extremely positive expectations are not met, peo-
ple become disillusioned and are more likely to
give up entirely. Minor lapses or setbacks are seen
in catastrophic terms, hastening disengagement or
dysfunctional forms of self-regulation. It would
have been better if they started with a more mod-
est outlook (Aspinwall, 2005).
To prevent this, expectancy has to be crafted in
a particular way, contingent on hard work or per-
severance. In other words, expectancy should not
be centered on the outcome being likely, which
sufficed during goal choice, but that despite inevi-
table and repeated obstacles and setbacks, we can
be confident that we have the internal resources
and depth of character to meet and overcome
them (Aspinwall & Taylor, 1997; Baumeister,
Heatherton, & Tice, 1994; Schwarzer, 2008).
Success will come, but after sometimes consid-
erable but not insurmountable effort. One way to
achieve this mindset is to increase task difficulty,
a core tactic of goal setting theory (Locke &
Latham, 2002).
Similarly, forces can switch for value. Ryan and
Deci’s (2000) self-determination theory and Frey
and Jegen’s (2001) motivational crowding theory
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The Building Blocks of MoTivaTion: goal Phase sysTeM 81
argues extrinsic rewards can crowd out or replace
intrinsic ones. Extrinsic rewards are likely to be
large and associated with goal choice, such as an
incentive plan. Intrinsic rewards are more subtle
and experienced by the individual during the very
act of goal striving, such as during a flow state
(Nakamura & Csikszentmihalyi, 2002). Consistent
with Allport’s (1937) functional autonomy of
motives, what reward originally makes us select as
a goal is not necessarily what propels us towards
achieving it. As reviewed by Steel and MacDonnell
(2012) or Freund and Hennecke (2015) under the
rubric of process versus outcome goals, there are a
variety of problems with treating the rewards that
helped establish goal choice as ones that should be
stressed during goal pursuit. Focusing on extrin-
sic rewards can be a form of positive fantasy,
which can be likened to motivational pornography
(Kappes & Oettingen, 2011). Fantasizing dimin-
ishes motivational energy as the image partially
satiates and reduces the need for the act itself. In
addition, since extrinsic rewards are received at
goal completion, emphasizing them not only shifts
focus from intrinsic rewards but also from the task
at hand. As Kanfer and Ackerman (1996) describe
in their resource allocation theory, when we are
thinking about more than what we are doing, we
are introducing competition for our limited cog-
nitive capacity. For any cognitively complex task,
emphasizing the rewards at completion is experi-
enced simply as a distraction (e.g., Ariely, Gneezy,
Loewenstein, & Mazar, 2009).
In a related manner, impulsiveness and delay do
not change in function during goal pursuit but def-
initely in standing. It helps explain the importance
of intrinsic motivators. While they may be small
compared to extrinsic motivators emphasized dur-
ing goal choice, they are experienced during the
act of goal pursuit and benefit from our impulsive
nature. Being immediately generated and con-
sumed, these intrinsic rewards are usually highly
valued. Impulsiveness and delay also largely
accounts for intention–action gaps and procras-
tination (Gustavson et al., 2014; Steel, 2007).
Where during goal choice and goal planning we
decide upon what we will do, impulsiveness often
gets in the way of doing it as planned. During pre-
vious phases, the time until implementation may
have been distant enough that temporal discount-
ing among competing tasks and temptations may
have seemed a non-issue. However, during goal
pursuit, it often becomes the choice between an
immediate alternative, often instantly pleasurable,
and the larger but later extrinsic reward. While
there are several factors that influence the degree
of implementation, such as the availability of
temptation, it is not until the task deadline nears
that forces start to favor consistency with goal
planning. The time remaining shortens, naturally
increasing the difficulty of the task, which in turns
heightens the amount of resources we allocate for
task completion. In addition, the target task’s out-
comes become increasingly short-term, meaning
that motivation is escalating moment by moment.
In some cases, this window of peak motivation
shrinks sufficiently fast that by the time people
start to act, the ever increasing task difficulty
quickly outstrips our capability, making the task
effectively impossible within the time available so
that it is abandoned or standards are sacrificed and
drastically lowered.
Finally, with goal choice and goal planning, we
were somewhat neutral regarding whether goals
were framed as approach or avoidance. There
should be a fit between regulatory focus and goal
(Scholer & Higgins, 2011), which is consistent
with behavioral theory (Schultz, 2006). Punishers
are best used to stop a behavior, while rewards are
better at creating action. To the extent our goals
represent what we want to achieve, there tends to
be an advantage to framing goals with an approach
orientation (Elliot & Friedman, 2006; Howell &
Watson, 2007). Also, inhibition or avoidance goals
can trigger ironic-processes, where we obsess
about what we are trying to prevent (Wenzlaff
& Wegner, 2000). In addition, avoidance goals
are resistant to forming specific deadlines, and
rarely benefit from temporal discounting (e.g.,
otherwise one must be soon not be doing some-
thing). Accordingly, techniques such as Applied
Behavioral Analysis (ABA) recommends that
instead of just trying to stop or punish a problem
behavior, try to also establish a replacement behav-
ior that takes its place (Hagopian, Dozier, Rooker,
& Jones, 2013). For example, focusing on starting
early is a preferable frame to not procrastinating.
Particularly relevant during goal striving is
effortful control, colloquially referred to as will-
power, which often manifests in our attempts to
remain consistent with our original intention or
plans. Because the role of cognitive resources in
goal striving and in particular self-control is begin-
ning to emerge in organizational psychology, we
provide an overview of the issues regarding this
work more generally in psychology. The general
model stipulates that motivational resources may
be depleteable and that exerting self-control itself
saps the individual of motivation. This is particu-
larly evident when we attempt to deploy cognitive
resources among a number of tasks simultane-
ously (Evans, 2008; Miller, 1956; Simon, 1955).
As a result, when individuals multi-task, they
experience fatigue and performance deficien-
cies (Baumeister, Vohs, & Tice, 2007; Kurzban,
Duckworth, Kable, & Myers, 2013), though cer-
tain tasks may exacerbate these effects more than
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others. In addition, Muraven and Baumeister
(2000) have proposed that this depletion effect
is analogous to muscular development, that after
exhaustion, given time, self-control will recover
and potentially at a heightened level of strength.
What resource is being depleted when people
exert self-control? Gailliot etal. (2007) proposed
and found initial support for the hypothesis that
glucose is being drained. While this has become a
dominant explanation for explaining self-control
and the experience of subjective effort in goal
striving, recent work has called aspects of the
model into question.
A series of studies re-examined Gailliot etal.’s
(2007) glucose explanation for depletion and found
it unreliable. Kurzban (2010) found the effect only
occurs for subjects who have fasted. Molden etal.
(2012) found across four experiments no support
for the glucose-model. Notably, these experiments
used more precise measures and tighter controls
than Gailliot et al.’s investigations. Job, Walton,
Bernecker, and Dweck (2013), replicating their
earlier work, confirmed that subsequent perfor-
mance only decreased for people who believe self-
control is a limited benefit and can be depleted,
indicating a nocebo (the opposite of a placebo)
effect. Converse and DeShon (2009) did not
find support for ego depletion when a three-task
design was used rather than the traditional two-
task design. Specifically, they propose that if indi-
viduals are given the opportunity to adapt to their
situation, depleting effects will disappear. Because
of these empirical findings, a number of people
have called into question the glucose theory and
proposed alternative theories that are motivation-
based, including Beedie and Lane (2012), Inzlicht
and Schmeichel (2012), and Kurzban etal. (2013).
We believe the model by Kurzban etal. (2013)
provides the best motivational account for under-
standing the phenomenology of effort because it
utilizes expectancy and value. Specifically, they
proposed, ‘that the sensation of “mental effort” is
the output of mechanisms designed to measure the
opportunity costs of engaging in the current men-
tal task’ (p. 13). Therefore, as individuals engage
in one task, they are intermittently calculating the
cost/benefit of engaging in other tasks (i.e., goal
choice). When there are valuable alternatives to the
focal task, individuals perceive that task as more
effortful and fatiguing because the alternative task
has higher value. As discussed in the next section
(i.e., dynamic theories of multiple-goal pursuit), this
is similar to Vancouver etal. (2010a) who proposed
that expectancy and value are changing dynami-
cally as individuals strive for multiple competing
goals. Building on this model by incorporating
impulsiveness, individuals who are high impul-
siveness may find striving towards their long-term
goals more fatiguing and effortful because alter-
native tasks that offer immediate rewards appear
more valuable. We recommend moving beyond the
depletion model and adopt a motivational account
for understanding the phenomenology of effort in
self-control and goal striving.
DYNAMIC THEORIES OF MULTIPLE-GOAL
PURSUIT: THE ROLE OF COMPUTATIONAL
MODELS
So far, motivational components are largely
treated as static or at the trait level. To fully
develop the GPS framework, this is not enough.
As van Gelder and Port (1994) stressed ‘Cognitive
processes and their context unfold continuously
and simultaneously in real time’ (p. 2). Yet, moti-
vational theories and research designs are largely
snapshots of dynamic phenomena with a single
criterion. The world outside is not stationary but
in flux, where individuals constantly interact with
their environment, and static theories may not
explain behavior adequately when applied to
this dynamic context. To address this gap in the
literature, there have been numerous calls in the
literature advocating a dynamic approach to
understanding organizational behavior (e.g., Dalal
& Hulin, 2008; Mitchell & James, 2001;
Sonnentag, 2012). These calls are beginning to be
answered by researchers examining work behav-
ior using longitudinal designs (e.g., Becker &
Cropanzano, 2011; Colquitt, LePine, Piccolo,
Zapata, & Rich, 2012; Ilies, Wilson, & Wagner,
2009; Kammeyer-Mueller, Wanberg, Glomb, &
Ahlburg, 2005; Schmidt & DeShon, 2007; Yeo &
Neal, 2006). In addition, multilevel modeling is
prevalent in all major journals. There is an inti-
mate relationship between statistical tools, empiri-
cal design, and theory. As our field uses more
dynamic tools, this will influence what theories
we rely on and how we collect data to adequately
test these theories.
Accordingly, Gigerenzer (1991) proposed that
the tools (often our statistical procedures) research-
ers use to account for some phenomenon become
our theories for that phenomenon. He calls this
the tools-to-theories-heuristic. There are numer-
ous examples of this throughout the history of
psychology. During Freud’s era, the steam engine
was used as a metaphor for how the mind works.
Then with the development of the computer, von
Neumann (1958) proposed that the brain could be
considered a digital computer. Gigerenzer suggests
that not only do these larger metaphors shape how
we think about the brain but that our statistical
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tools also shape how we think about psychologi-
cal phenomenon. For example, he discusses how
signal detection theory is theoretically grounded in
Neyman-Pearson hypothesis testing. Signal detec-
tion theory was based on new tools (statistics) not
widely used within the field at the time and there-
fore required new data (Tanner & Swets, 1954).
Thus, data did not drive the theory, but rather the
tools and the theory required new data. In the orga-
nizational literature, with the increased use of mul-
tilevel modeling (tools), our theories and research
designs can now account for nested and repeated
measures data, which was out of our reach 30
years ago. From this perspective, over the next
20 years computational models (tools) will influ-
ence our theories greatly. With the increased use of
computational modeling, our theories will become
more dynamic, more precise and with multilevel
modeling, our research designs will be able to
account for such complex and dynamic theories.
Consequently, we briefly define computational
models, why they are necessary, and what advan-
tages they have. After this foundation, we review
computational models in motivation and make
suggestions of theories that should be integrating
with current computational models.
Computational models are algorithmic descrip-
tions of process details, typically operationalized
as computer programs that are dynamic and can
be simulated (Taber & Timpone, 1996). The goal
of computational modeling is to create a represen-
tation of the system-in-context that approximates
the underlying process of the phenomenon we
as researchers are trying to understand (Myung,
2003). Perhaps more importantly, computational
models can be simulated, allowing researchers to
examine how phenomena evolve over time. This
is difficult to duplicate using other tools because
of limitations in verbal language and the human
mind. When developing a computational model, it
is necessary to describe the relationship between
the variables mathematically. Unlike verbal theo-
ries, there is little ambiguity regarding the theory.
And through our own mental modeling we may
believe we can easily understand dynamic phe-
nomenon, research shows that even well-educated
individuals from STEM degrees at some of the best
institutions in the United States of America do not
effectively understand dynamic systems (Cronin,
Gonzalez, & Sterman, 2009; Navarro & Arrieta,
2010; Sterman, 1989; Weinhardt, Hendijani,
Harman, Steel, & Gonzalez, in press). Necessarily
then, as our field begins to develop dynamic theo-
ries and use dynamic empirical designs, computa-
tional models are required to help researchers test
the validity of their theories and designs.
As computational modeling is adopted, a
number of benefits will accrue. Specifically,
computational models can be used for theory
building (Ilgen & Hulin, 2000; Vancouver etal.,
2010a), formally describing and testing parts of
an existing present theory (Vancouver, Putka, &
Scherbaum, 2005), resolving conflicting theo-
retical issues (Vancouver & Scherbaum, 2008;
Vancouver & Weinhardt, 2012), integrating theo-
ries (Steel & König, 2006; Vancouver etal., 2014),
and resolving conflicting empirical findings
(Vancouver, Tamanini, & Yoder, 2010b). Aguinis
and Vandenberg (2014) have provided a review of
steps researchers should do before data collection.
We propose that computational models should be
one of the steps researchers embrace. Now that we
have outlined broadly the importance of computa-
tional models, we will turn to computational mod-
els regarding motivation.
While a large recent proportion of motivational
computational models has been done by Vancouver
and colleagues (e.g., Vancouver & Scherbaum,
2008; Vancouver et al., 2005; Vancouver et al.,
2010a; Vancouver & Weinhardt, 2012; Vancouver
etal., 2014), there are several other contributors,
such as DeShon and Gillespie’s (2005) motivated
action theory which integrates aspects of control
theory with expectancy × value. Rather than dis-
cussing each model in detail, we focus on com-
putational models dealing with dynamic multiple
goal regulation and propose how work on affect
could be integrated with these models to further
motivation research.
In our daily lives, we are constantly regulating
multiple goals over time and must regularly choose
where to allocate resources among short-term and
long-term goals. While some excellent work has
been done, such as by Schmidt and colleagues
(Schmidt & DeShon, 2007; Schmidt, Dolis, &
Tolli, 2009), the field is rather limited in regards
to research on multiple-goal motivation. To rec-
tify this, Vancouver et al. (2010a) developed a
dynamic computational model that addresses how
individuals strive for two competing goals over
time, which included dynamic expectancy, value,
and temporal components. Notably, not only are
there reversals for expectancy, which has positive
effects for goal choice and negative effects for
goal planning, there is competition among these
tasks. In the model, as individuals begin to work
on a goal, their expectancy is changing due to a
reduced discrepancy between where they are now
and what they want to achieve; having accom-
plished more, they have higher expectancy that
the goal will be reached. Meanwhile, their expec-
tancy for the other goal is low but the relative need
(i.e., value) is increasing as more remains to be
done. Therefore, they switch to the goal with the
greater need, despite having the lower expectancy.
This back-and-forth switching continues, until the
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deadline approaches. Near the deadline, assisted
by hyperbolic discounting, it becomes clear that
it is difficult to achieve both goals, whereupon
expectancy dominates as individuals favor the task
they are most certain of completing to ensure at
least one goal is achieved.
As outlined above, one of the advantages of
computational models is that they can easily incor-
porate different motivational components and for-
mulations. Vancouver etal. (2010a) were able to
model dynamically expectancy and value using a
control theory framework and integrated this with
hyperbolic discounting from temporal motivation
theory. For example, one of the regulatory agents
in the model was called a time agent, which con-
tinuously monitored the time until deadline. They
also modeled people’s impulsiveness, their sensi-
tivity to deadlines. Individuals who are more sen-
sitive to deadlines will be more likely to switch
earlier to the goal that is more easily achievable.
Demonstrating its flexibility, Vancouver et al.
(2014) later expanded on the model by integrat-
ing cognitive psychology theories of learning with
theories of motivation. In this expansion, biases in
time perception were incorporated and how they
may arise as individuals regulate multiple compet-
ing goals. The resulting model was able to account
for how an individual learns to regulate multiple
competing goals over time and can account for
various differences in regulation of multiple
competing goals, including the planning fallacy
(Buehler etal., 1994).
Although these models have led to greater
understanding of dynamic multiple goal regula-
tion, there is still room for further expansion.
Specifically, these models do not incorporate
work from the affect literature. Consequently, a
natural next step would be to incorporate Carver
and Scheier’s (1998) ideas about the affective sys-
tem, who propose that an affective self-regulatory
system runs in parallel with the behavioral self-
regulatory system. This affective system has two
main functions. The first function uses affect as
a signal in the behavioral system about the rate
of discrepancy reduction between the current state
and the goal state (i.e., goal progress). If the rate
of progress is below a certain criterion, the indi-
vidual experiences negative affect, which signals
the need for more resources to be allocated to
achieving a goal. If goal progress is above a cer-
tain criterion the individual experiences positive
affect which indicates that fewer resources need to
be applied to the focal goal, which they refer to as
coasting. The other function of the affective self-
regulatory system is to signal goal reprioritization,
as postulated by Simon (1967). Where negative
affect serves as a signal to allocate more resources
towards a goal, positive affect on the other hand
serves as a signal that resources do not need to be
applied to this goal, and therefore the individual
is more likely to engage in another goal. Carver
(2003), in a more thorough examination of goal
prioritization, theorized that positive affect is the
mechanism that leads an individual to switch from
one goal to another. However, very little empiri-
cal research has been done regarding this second
function of the affective system.
To better address affect regulation, more work
should be done in line with Beal, Weiss, Barros,
and MacDermid’s (2005) dynamic episodic pro-
cess model, which examines several intercon-
nected elements that simultaneously impact
performance. In general, emotion is inherently
suited to a computational modeling approach as
affect can be both an antecedent of motivation,
such as influencing perceptions of risk (Slovic,
Peters, Finucane, & MacGregor, 2005), and an
outcome, requiring a feedback loop (Schmidt
etal., 2013). In addition, there should be continued
efforts to link motivation with emotion regulation
and emotional labor. There are strong parallels
between motivational and emotional regulation,
though the latter may be quicker. For example,
Vancouver and Weinhardt (2012) computationally
modeled the regulation of well-being and stress
using the same self-regulatory framework found in
theories of motivation. Also, researchers often dis-
cuss the outcome of emotional labor as emotional
exhaustion, which is the same nomological net of
the ‘depletion’ effects described by self-control
researchers (Beal et al., 2005). Consequently,
computational models should help integrate the
work on emotional regulation and labor with moti-
vation to better account for the dynamic processes
unfolding throughout the day.
Motivation is a dynamic and complex process
and can be examined both from its operations as
well as its outcomes. Though there are constrained
numbers of major components, they interact and
change over time, making computational mod-
els an especially useful tool for capturing moti-
vational dynamics. If we want the field of work
motivation to be considered in the same conver-
sation as other sciences, it appears that we need
to develop and test our theories computationally
(Harrison, Lin, Carroll, & Carley, 2007; Ilgen
& Hulin, 2000; Vancouver & Weinhardt, 2012;
Weinhardt & Vancouver, 2012).
MOTIVATIONAL INTERVENTIONS
In our efforts to improve motivation, there gener-
ally has been little understanding of the complete
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array of motivational components, phases, or its
dynamic nature. This is most easily seen in our
attempts to design incentive programs. As Fryer
(2010) discovered while attempting to motivate
student achievement across 250 urban schools, ‘In
stark contrast to simple economic models …
incentives tied to output are not effective’ (p. 2).
He suggests several reasons, including that there
were failures during goal striving (i.e., ‘lack of
self-control’). The major point being is that expec-
tancy × value models operate best at determining
goal choice and are less appropriate for subse-
quent goal phases. Similarly, in a pair of studies,
Pepper and Crossman evaluated how long-term
incentives for executives, which comprise almost
50% of their total earnings, were being imple-
mented compared with the underlying motiva-
tional forces. Typically, as they note, this
compensation scheme relies on a very basic or
stripped-down rational or expectancy × value
model. Adopting the more complex temporal
motivation theory, they found the way ‘senior
executives assess probabilities and value is signifi-
cantly affected by risk aversion, time discounting
and uncertainty aversion’ (Pepper, Gore, &
Crossman, 2013, p. 48), a finding they replicated
cross-culturally and concluded that despite their
widespread use, ‘long-term incentives are not an
efficient way of motivating senior executives,
irrespective of national culture’ (Pepper & Gore,
2014, p. 359).
To effectively apply GPS, we need to take a
more sophisticated approach to motivational inter-
ventions. To do this, we need to understand where
motivational interventions are best applied by con-
necting them to the appropriate goal phase and sub-
component as well as considering their dynamic
nature, all of which can be done (Vancouver,
2008; Vancouver, et al., 2014). As Gröpel and
Steel (2008) argued, this includes moving ‘beyond
motivational main effects and towards customiz-
ing interventions to the individual’ (p. 410). They
recommend developing diagnostic procedures,
such as the Motivational Diagnostic Test (Steel,
2011), that identify people’s particular motiva-
tional vulnerabilities (e.g., impulsiveness) or gaps
in skill repertoires (e.g., poor goal setting). This
will enable precise matching of the intervention to
the individual, increasing the efficacy of our moti-
vational treatments.
Also, this integrative approach has important
implications for coordinating employee-based
interventions. A variety of motivational training
programs, despite having different theoretical
origins, are overlapping in practice, advocating
essentially identical techniques. For example,
interventions for emotional intelligence, aside
from the construct’s disputed theoretical or
measurement independence (Joseph & Newman,
2010; O’Boyle, Humphrey, Pollack, Hawver, &
Story, 2011; Walter, Cole, & Humphrey, 2011),
often stress improving the fundamental moti-
vational elements of impulse control and self-
confidence (Clarke, 2006; Goleman, 1998).
Almost regardless of a self-regulatory interven-
tion’s espoused heritage, the core and most effec-
tive features can be largely classified as operating
on a particular goal phase and motivational com-
ponent (i.e., expectancy, value, time, and approach
versus avoidance), often in a dynamic fashion. We
review these interventions here.
During Goal Choice
Attempts to improve decision-making come under
the rubric of ‘Choice Architecture’. While we
have been very successful at identifying sources
of biases during goal choice and exploiting them
during consumption, creating ‘patches’ for our
faulty decision-making software has not kept
pace. As Payne, Bettman, and Schkade (1999)
discuss, we have yet to develop a ‘building code’,
which is a set of universally accepted guidelines
that help construct optimal preferences. Still,
basic heuristics have been developed, particularly
removing the effect of impulsiveness through the
mechanism of enforced delays. This class of self-
control techniques that delay action belongs to is
precommitment. Precommitment may also occur
during goal planning, but given pre-commitment’s
commonality with commitment, we discuss it
here.
Precommitment has a long history, stretching
back at least to the ancient Greek story of Ulysses
(Bryan, Karlan, & Nelson, 2010). In a form of
anticipatory self-command, we act now in order
to prevent ourselves from acting otherwise later.
Ulysses, for example, used this principle to bind
himself to his ship’s mast so he couldn’t later
respond to the Sirens’ song. There are a variety of
modern-day variations, such as freezing a credit
card in a block of ice (which must be thawed) or
Clocky, an alarm clock on wheels that evades its
owner (Steel, 2010). Notably, the very structure
of most governments is built to prevent impulsive
choice, such as the use of bicameralism, which
requires legislation to pass through two houses
(e.g., a senate and a congress). This structure was
explicitly designed to foster more deliberative and
cooler decision-making (Hamilton, 2004; Steel,
2010).
More comprehensive processes to improve goal
choice have proved elusive, but work has been
done. There are two active support protocols that
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assist in making decisions more consistent with
our values, even in situations of considerable com-
plexity. First, multiple criteria decision-making or
multi-attribute utility theory, which has origins in
operations management, is explicitly designed to
help us determine our preferences in situations
where the classical or rational expectancy × value
model has less hold (Figueira, Greco, & Ehrgott,
2005). Most typically, this happens when there
are multiple conflicting criteria with a variety of
tradeoffs, creating goal conflict (Emmons, King,
& Sheldon, 1993). Second, incorporating and
building on multi-attribute utility theory, struc-
tured decision-making is a bundle of techniques
that include steps that mitigate cognitive biases,
such as the tendency to adopt the present state
of affairs (i.e., status quo bias) as being the best
possible (Gregory etal., 2012). Both techniques
require a degree of preparation that make them
difficult to casually use, though many applica-
tions exist for more pivotal and established life
events, such as choice of pension plan, with adop-
tion further eased, as the International Society on
Multiple Criteria Decision Making documents,
through dozens of decision-making software pro-
grams. Through a combination of these two fac-
tors, for example, they have also made significant
inroads into environmental decision-making, such
as choice of energy source or sustainability plans
(Arvai, Campbell-Arvai, & Steel, 2012).
During Goal Planning
Allocation of appropriate resources during goal
planning is compromised by the planning fallacy,
where we underestimate the amount of resources
necessary for goal success. This is not easy to
remedy and the majority of interventions that
sought to prevent it have failed (Roy etal., 2005).
However, it can be significantly reduced if we
acknowledge our internal mental biases and com-
pensate for them with explicit external processes.
Roy etal. propose a method that improves estima-
tion by keeping strict objective records of how
long projects take and relying on these to deter-
mine tasks time. Similarly, Bishop and Trout
(2008) recommend when determining how long it
takes to write a paper to look back at one’s CV and
determine how many articles we have previously
published per year. Another suggested mechanism
for guarding against over-commitments is to take
the ‘outside view’, where we assess how long it
would take for someone else to finish the project
(Lovallo & Kahneman, 2003).
Goal planning is not limited to simply sched-
uling, however, where we just rationally allocate
time and resources so we can tackle the task at
hand. It also has meta-cognitive aspects, where we
anticipate our motivational weaknesses and plan
how to overcome them (Gollwitzer & Oettingen,
2011). Unfortunately, these same weaknesses
reduce our capacity to plan. Those overconfi-
dent in their self-regulatory strength and their
degree of certainty are less likely to take steps to
mitigate problems they don’t even acknowledge
existing. Those who don’t value self-regulation
aren’t likely to self-regulate. And, while there are
many techniques available to reduce impulsiv-
ity, those most impulsive are least likely to use
them. Of the available techniques, many of them
have been codified under the inductive theory of
goal setting. This is almost a form of motivational
reverse-engineering as we know that motivation
coalesces just before most naturally occurring
deadlines. The question then becomes: What are
these attributes that create this motivation and can
we input them into artificially created goals of our
own choosing?
Goal setting is consistent and derivable from
the motivational fundamentals outlined in tempo-
ral motivation theory (Gröpel & Steel, 2008). It
counsels us to make goals that: we are commit-
ted to, are difficult but achievable, are specific
and proximal (Locke & Latham, 2002). Notably,
the theory has one area of tension, between goal
commitment and difficulty, as per ‘expectancy is
said to be linearly and positively related to per-
formance. However, because difficult goals are
harder to attain than easy goals, expectancy of goal
success would presumably be negatively related to
performance’ (Locke & Latham, 2002, p. 706).
This conflict can be readily resolved through the
use of goal phases. Goal commitment, which has
positive relationships with expectancy and value,
applies to goal choice while goal difficulty, which
by making the task harder decreases expectancy,
applies to the subsequent two phases.
Expectancy is central to two other motivational
interventions. Notably, goal commitment moder-
ates one motivational technique during goal plan-
ning. If there is sufficient goal commitment, goal
planning is further assisted by mental contrasting
(Kappes, Singmann, & Oettingen, 2012). Mental
contrasting is where we vividly imagine our goal
completion and its benefits, similar to positive
goal fantasy. It differs from the latter by adding
the additional step of reflecting with equal vigor
on our present situation and its accompanied chal-
lenges and obstacles. If commitment remains
strong, planning typically ensues. The false hope
syndrome can also be mitigated during goal plan-
ning. Sitkin’s (1992) strategy of small losses,
whereby we learn from failure but construct our
goals to contain failure so as to incur only small
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The Building Blocks of MoTivaTion: goal Phase sysTeM 87
losses, is a useful framework for guarding against
disengagement caused by minor setbacks.
Other goal setting techniques primarily deal
with our excessive impulsivity and can be largely
interpreted as translating goals chosen with a
deliberative mindset, associated with the prefron-
tal cortex, into motivational terms receptive to the
implemental mindset, associated with the limbic
system. Goal setting theory states that specific and
proximal tasks tend to be pursued with more vigor
than vague and distal ones, an observation that
it largely shares with construal theory (McCrea,
Liberman, Trope, & Sherman, 2008). This is
exactly the type of phenomena that the limbic sys-
tem attends to, the nearby and the concrete. So,
the goal of ‘writing a book’ may result in delay
but the goal of ‘writing 300 words today on chap-
ter three’ is much more likely to result in action.
This will result in effort being more spread out,
which should help to alleviate the stress involved
in working close to the deadline.
Several techniques used during motivational
planning prevent the limbic system from re-evalu-
ating goal choice, helping to ensure that goal striv-
ing continues. Automatization or routine building
is a particularly successful application of this
principle. We are susceptible to impulsivity when
making decisions so one way to avoid impul-
siveness is to avoid making decisions altogether.
While almost half of our daily behaviors are really
well-rehearsed routines that we re-enact with little
thought (Ouellette & Wood, 1998), these scripts
can be usefully cultivated to prevent impulsive
choice, enabling us to persevere towards our goals
instead of succumbing to temptation (Baumeister,
Muraven, & Tice, 2000). Implementation inten-
tions have proven to be particularly useful in this
regard (Gollwitzer & Oettingen, 2011). They
resemble stimulus–response conditioning, where
we specify actions to be done contingent on a
trigger or situation. Often portrayed in a mad-lib
fashion, we state, ‘If (situation X), then I will
do (action Y)’. A particularly useful autological
example of them is ‘If I am pursuing a goal, then
I will use implementation intentions’. Notably,
these techniques have been successfully combined
with mental contrasting (i.e., the MCII technique).
Consistent with them operating on separate com-
ponents of motivation, used together they are bet-
ter than each alone (Oettingen, 2012).
During Goal Striving
Successful goal striving is often predicated on
steps taken during goal planning. When actually
engaged in the task, fewer options remain.
To differentiate these from goal planning, these
techniques must be initiated and applied currently
or in parallel with the actual pursuit. What aspects
of ongoing events one focuses on and how these
events are interpreted influence the perception and
effects of expectancy, value, and impulsiveness.
Broadly, there are two classes of motivational
interventions that accomplish this: cognitive and
attentional.
Cognitive therapy aims to shift how we think
and feel in order to change behavior. There are a
variety of different forms, but two are particularly
relevant in a work motivational context, both draw-
ing on attribution theory and explanatory style.
The first is learned optimism (Seligman, 2011),
where we improve expectancy in the face of fail-
ure by attributing setbacks to temporary, situation
specific, and external forces (rather than perma-
nent, pervasive, and internal causes). The second
is cognitive evaluation theory, a subset of self-
determination theory (Deci & Ryan, 1985). To the
extent we can emphasize feelings of competence
and autonomy during goal striving, intrinsic moti-
vation is enhanced. These feelings are influenced
by our general causal attributional style, so we can
improve our intrinsic motivation by emphasizing
volition, even if it is simply choosing between two
work-related tasks, and connecting what we do to
our more fundamental needs, such as achievement
or affiliation.
The second class of goal striving motivational
interventions can be labeled attentional control.
This has features similar to stimulus control,
where external cues trigger specific behaviors.
However, with attentional control, these cues are
either enhanced or redirected cognitively away
from distracting temptations and/or towards our
target goal. Highlighting how basic or fundamen-
tal is this technique, the foundational research
done in this area is with children and chimpan-
zees. Mischel and Baker’s (1975) seminal work
on cognitive reappraisals examined how children
can use them to assist in delaying gratification.
Instead of avoiding thinking about temptations
(i.e., irrelevant non-goal options), we can mentally
distance ourselves by focusing on their ‘cooler’
non-consumptive aspects (Mischel & Ayduk,
2004). To do this, temptations are framed in terms
of their abstract and symbolic features, such as
Mischel having children focus on pretzels’ shape
and color, (e.g., ‘the pretzels are long and thin like
little logs’), rather than their texture and taste.
In a strikingly similar study, the anthropologist
Deacon (1997) trained chimps to use lexigrams to
make food choices. Lexigrams are symbolic repre-
sentations, such as kiwis being depicted by a black
square with a blue ‘Ki’ and strawberries depicted
by a red square with two horizontal white lines.
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88
The chimps had to point to one of these options
and, importantly, get the fruit they didn’t select.
Chimps trained with lexigrams soon learned how
the game worked and chose the less desirable
options but if confronted with actual bowls of
fruit, not their symbolic stand-ins, were unable to
override their initial instincts. In Deacon’s words,
this symbolic representation is vital for self-con-
trol, for without it ‘being completely focused on
what they want, they seem unable to stand back
from the situation, so to speak, and subjugate their
desire to the pragmatic context’ (1997, p. 414).
Basically, symbols tip our choices away from the
concrete and stimulus-driven limbic system and
back to the abstract plans of the prefrontal cortex
(Gifford, 2002).
CONCLUSION
We used this chapter to take a moment of pause,
to reflect on what major motivational elements our
field has consistently found, and then assembled
these pieces together into a coherent whole: the
Goal Phase System (GPS). This framework moves
us towards a ‘boundaryless’ science of motivation
(Locke & Latham, 2004), one that can be informed
from multiple disciplines and applied to a wide
variety of problems. In absence of this broader
view, our present perspectives are both too sim-
plistic and too complicated. Too simplistic in that
each motivation theory only accounts for a small
part of the motivational process, and we tend to
have little appreciation of these limits. Too com-
plicated in that we have far too many theories,
with many of them being functionally identical or
a recombination of more basic elements.
Considerable confusion has arisen as we treat
theories that focus on motivational subsections
(e.g., expectancy × value) or goal phases (e.g.,
goal choice) as being more comprehensive than
warranted and then inappropriately overextend
them. While the integrated view provided here is
a remedy, we will still need to employ it strategi-
cally, balancing parsimony with completeness.
As Einstein suggested ‘Make everything as
simple as possible, but not simpler’. When draw-
ing on the motivational features here, it is unlikely
that we need to use all aspects of GPS for all
research questions or applied situations. We may
be focusing on a particular goal phase and specific
motivational aspects, such as impulsiveness or
time, may be effectively constants in our situation,
allowing them to be dropped from the analysis. Or,
we may be dealing with a participant group that is
selected for a precise motivational weakness, such
as lower self-confidence or impulsiveness. Our
motivational interventions should be appropriately
targeted to their motivational weaknesses, a match-
ing presently we rarely consider (Gröpel & Steel,
2008). Other times, a more sophisticated battery
of concepts is needed. For example, Richardson
and Taylor (2012) used almost all aspects
discussed here – temporal motivation theory,
goal phase theory, and computation modeling –
to help understand how employees respond to
requests for their input.
While not all aspects of motivation are always
needed, we do need to always consider all motiva-
tional aspects. To justify simplification, we need
first to cogently consider and argue which features
of GPS are operating and which are not. As we
stressed at the start, this is part of the conscious
guidance that Heath (2014) was calling for. And,
based on this understanding, perhaps we can avoid
the accelerating slide towards dysfunctional deci-
sion-making that Heath and all the others warned
us about. We can learn self-regulatory techniques,
create motivational schemes, and build environ-
ments that coax us towards better goal choices,
better goal plans, and superior goal striving.
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BK-SAGE-ONES ET AL -170181-Chp04.indd 96 15/11/17 12:50 PM
... The costs and benefits of pursuing one goal must be weighed against those of pursuing various other alternatives. Two key determinants are relevant for these considerations: the expectation that one will be able to perform the behavior that leads to the desired outcome successfully and the subjective value attached to that outcome (Atkinson, 1957;Gollwitzer, 1990;Bandura, 1997;Eccles and Wigfield, 2002;Locke and Latham, 2002;Steel and Weinhardt, 2018). The higher the subjective value of the anticipated outcome and the expectation that goaldirected behavior can be successfully implemented, the higher the willingness of the person to invest effort and to translate an intention into action (Brehm and Self, 1989;Gollwitzer, 1990;Klein et al., 1999;Eccles and Wigfield, 2002;Dietrich et al., 2017). ...
... The influence of the personal value attributed to the achievement of academic tasks has received surprisingly little attention in previous studies on potential determinants of procrastination. This is particularly astonishing because task value is explicitly emphasized in theoretical explanations of the origins of dilatory behavior (e.g., Steel and König, 2006;Glick and Orsillo, 2015;Steel and Weinhardt, 2018). In line with theoretical assumptions, our study revealed that tasks to which students initially attributed an above-average value were significantly less likely to be delayed. ...
... Although our results provide evidence that the occurrence of delay behavior was associated with a momentary devaluation of the task, we cannot draw conclusions about why students' initial task-specific appraisals have changed. Following the assumptions of Temporal Motivation Theory (TMT; Steel and König, 2006;Steel and Weinhardt, 2018), it is quite possible that the devaluation of a task resulted from a direct comparison with a potentially more attractive alternative activity. However, the present investigation was not supposed to and cannot provide evidence for the temporal discounting principle proposed in TMT (Steel and König, 2006;Ainslie, 2012), as no comparison with an alternative activity was made. ...
Article
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Academic procrastination involves the delayed implementation of actions required to fulfill study-related tasks. These behavioral delays are thought to result from momentary failures in self-regulation (i.e., within-person processes). Most previous studies focused on the role of trait-based individual differences in students’ procrastination tendencies. Little is known about the within-person processes involved in the occurrence of procrastination behavior in real-life academic situations. The present study applied an event-based experience sampling approach to investigate whether the onset of task-specific delay behavior can be attributed to unfavorable changes in students’ momentary appraisals of tasks (value, aversiveness, effort, expectations of success), which may indicate failures in self-regulation arise between critical phases of goal-directed action. University students (N = 75) used an electronic diary over eight days to indicate their next days’ intentions to work on academic tasks and their task-specific appraisals (n = 582 academic tasks planned). For each task, a second query requested the next day determined whether students’ task-related appraisals changed and whether they implemented their intention on time or delayed working on the respective task (n = 501 completed task-specific measurements). Students’ general procrastination tendency was assessed at baseline using two established self-report questionnaires. Stepwise two-level logistic regression analyses revealed that within-person changes in task-related appraisals that reflected a devaluation of the study-related tasks increased the risk for an actual delay. The risk to delay decreased when students maintained a positive attitude toward the task. Students’ general procrastination tendency did not predict individual differences in their task-specific delay behavior. We discuss these findings in light of the growing effort to understand the within-person processes that contribute to induce procrastination behavior under real-life academic conditions and illustrate how this knowledge can benefit the design of tasks and instructions that support students’ self-regulation to their best. THIS ARTICLE IS PART OF THE RESEARCH TOPIC: New Perspectives on Procrastination, Volume II
... According to Ferster & Skinner's (1957) reinforcement theory, behavior can be strengthened by either delivering a positive outcome or removing a negative outcome when the task is completed. Accordingly, many researchers also believe that it is the future rewards and punishments that motivate people to engage in a task as soon as they can (Steel, 2007;Steel & Weinhardt, 2017;Strunk, Cho, Steele, & Bridges, 2013). To this day, the strongest emphasis on those incentives was proposed by a 2 × 2 model of time-related academic behavior (henceforth: the 2 × 2 model; Strunk et al., 2013). ...
... Among these four components, the value is the hardest one to define. In the latest version (Steel & Weinhardt, 2017), the temporal motivation theory expanded its definition of task value by incorporating extrinsic and intrinsic motivations (Ryan & Deci, 2000). For example, Steel and Weinhardt noted that "Consequently, we are extrinsically motivated when we are driven by the outcome of our actions, such as for financial incentives, but intrinsically motivated when driven by rewards inherent in the task itself." ...
... Procrastination is defined as the purposive delay of an intended course of action despite the expectation to be worse off for the delay (Steel, 2007). Procrastination can occur in all phases of the motivational process: during goal choice, goal planning, and goal striving (Steel & Weinhardt, 2017; see also Krause & Freund, 2014b). The current research addresses procrastination during goal striving (in this case, writing a bachelor's thesis), that is, during the actional phase in the Rubicon model of action phases (Gollwitzer, 1990;H. ...
... versus goal striving (e.g., "Now that I am working out, how long should I keep going?"; see Steel & Weinhardt, 2017). On the other hand, a focus on one particular alternative activity may come at the price of limited generalizability to other activities. ...
... Although these students are presently procrastinating and are avoid work on the project, they expect to complete it in the future, so long as it promises a greater degree of benefit upon its completion. This model explains procrastination as an increase in motivation the closer an assignment's due date approaches (Steel, 2007;Steel & Köning, 2006;Steel & Weinhardt, 2018). ...
Article
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This qualitative study examines academic procrastination among Israeli Master of Education students writing their theses. The majority of the the participants (80% of the 145) reported behaving differently on this task than on other assignments. One of the primary factors influencing procrastination derives from the complexity of the assignment. Considering the research literature describes tight relationships between academic procrastination and academic achievement, one surprising finding concerns the fact that respondents saw no relationship between their procrastination and their final grade. A gap was found between students' self-perception and their actual performance. Approximately 75% of the students perceive themselves as academic procrastinators, but in actuality nearly half of them completed the assignment on time. The starting date was found to be significant. Students who immediately began work upon receiving the assignment strongly tended to submit it on time. Students who did not begin early completed the project later than the scheduled date, if at all. Practitioner Notes Practitioner Notes 1. There are challenges to responding to student procrastination 2. Procrastination has a direct effect on student achievement 3. There is a gap between students' self-perception and their actual performance. 4. The complexity of the assignment has an effect on procrastination
... Being organized enhances the ability to set proximate goals, which dependably increases motivation (Locke and Latham, 2004). This effect directly follows from shortening the Delay variable in TMT (Steel and König, 2006;Steel and Weinhardt, 2018). For example, Renn et al. (2011) as well as Gröpel and Steel (2008) confirmed the negative relationship that procrastination has with goal setting, organizing and other forms of self-management. ...
Article
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We investigated the causes and impact of procrastination on “slippery deadlines,” where the due date is ill-defined and can be autonomously extended, using the unique applied setting of grievance arbitration across two studies. In Study One, using 3 years of observed performance data derived from Canadian arbitration cases and a survey of leading arbitrators, we examined the effect of individual differences, self-regulatory skills, workloads and task characteristics on time delay. Observed delay here is a critical criterion, where justice is emphasized to be swift and sure. Multilevel Modeling established trait procrastination as a substantive predictor of observed delay, equivalent to the environmental contributors of expediting the arbitration procedure or grievance complexity. Also, despite substantive negative consequence of delay for both arbitrators and their clients, arbitrators who scored one standard deviation above the mean in procrastination took approximately 83 days to write their decisions compared to the 26 days for arbitrators one standard deviation below the mean. In Study Two, we conducted a replication and extension survey with a much larger group of American arbitrators. Consistent with Temporal Motivation Theory (TMT), trait procrastination was largely explained by expectancy, value, and sensitivity to time related traits and skills, which together accounted for majority of the variance in trait procrastination, leaving little left for other explanations. For example, perfectionism connection to procrastination appears to be distal, being largely mediated by each of TMT’s core variables. Finally, procrastination was largely synonymous with a deadline pacing style, indicating that observed delay can be used as a proxy for procrastination as long as little or no prior work was done (e.g., a u-shaped pacing style is not synonymous). In all, our results indicate that procrastination is rampant in the workplace and has seriously detrimental effects.
... While we tend to have good basic theories, what we tend to lack is a clear understanding of boundary conditions, where a simpler theory would be preferred to a needlessly more complex one (Steel & Weinhardt, 2018). Expectancy theory highlights rational components of decisionmaking while self-determination and motivation crowd-out theories emphasize how choice is an important motivational consideration. ...
Article
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The effect of performance-contingent reward and choice on motivation and performance continues to be debated. Studies in economics and behavioral psychology consider performance-contingent rewards and choice as two separate motivational mechanisms that reinforce motivation and performance. However, theories on self-determination and motivational crowding predict that performance-contingent rewards negatively interact with choice, reducing its positive effect on motivation. The conceptual and methodological differences between these streams suggest a more nuanced approach that considers factors including reward salience and task type. Building upon attribution theory, we designed and conducted an experiment to test the effect of choice (choice vs. no-choice) and reward (salient, non-salient, and no reward) on overall motivation and performance. Non-salient reward and choice interacted in a positive way, resulting in motivation and performance improvement, what we describe as a “Motivational Congruence Effect.” Similarly, salient reward in a no-choice condition had a positive effect on motivation and performance.
... Another important step in validating the differentiation between onset procrastination and procrastination in the goal-striving phase is to investigate whether the two facets relate differently to motivational and volitional variables. As noted, the motivational forces operating during the beginning of goal pursuit are not necessarily the same as those important during later goal striving (Steel & Weinhardt, 2018). Thus, motivational variables, such as expectancies and values, should be related strongly to onset procrastination, whereas volitional variables, such as the ability to shield distractions or willpower in general, should relate more strongly to procrastination in the goal-striving phase. ...
Article
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Scales measuring procrastination focus on different aspects of unnecessary and unwanted delay, delay in task implementation – an increased gap between intention and action – being a core characteristic. However, an inspection of existing procrastination scales reveals that the scales do not distinguish between two facets of implemental delay, onset delay, and delay related to sustained goal striving. We trace this failure to an imprecise understanding of “delay,” another core concept in procrastination. This paper discusses the relationship between onset and sustained delay in procrastination, and then describes a new scale attempting to measure these two facets of task implementation. In two studies (aggregated N = 465) we demonstrate, using exploratory and confirmatory factor analysis, that although onset and sustained action procrastination measures correlate, they are still separate facets of implemental procrastination. Problems with onset delay seem to be particularly important, increasingly so in high procrastinators. Implications, as well as suggestions for further research, are discussed.
... That is, people find a task so aversive that they would rather avoid it at the expense of long-term goals to feel good temporarily. On the other hand, the temporal motivational theory explains procrastination as the increase of motivation to act when time moves toward a deadline (Steel, 2007;Steel & König, 2006;Steel & Weinhardt, 2017). More specifically, motivation to act increases when people have confidence in acquiring (i.e., expectancy) a desired outcome or reward (i.e., value), whereas this motivation declines when there is a large amount of time before the reward is realized (i.e., delay) and when people are sensitive to delays (i.e., ⌫) (see Equation 1). ...
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Although procrastination has troubled people consistently, there is a lack of systematic theories to explain this behavior. The present study aims to propose and validate a temporal decision model to explain procrastination. The temporal decision model predicts that people will procrastinate on a task so long as the aversiveness of a task outweighs the utility of future incentive outcomes that this task can yield. Specifically, people perceive less aversiveness from a task when this task is scheduled in the future than in the present but expect that they can perceive higher utility from the incentive outcome in the future than in the present. Consequently, people are reluctant to do this task in the present but expect that they are willing to do it in the future (i.e., procrastination). We tested these predictions by measuring perceived task aversiveness, outcome utility, and decision for real-life tasks when the same tasks were scheduled with different delays. The results demonstrate that people expect that they would procrastinate a task as long as perceived task aversiveness is stronger than outcome utility and would stop procrastinating when perceived task aversiveness becomes comparable with outcome utility. Furthermore, people perceive less task aversiveness when the task is scheduled further away and expect that outcome utility would be higher when time gets closer to the delivery of outcome in the future. The present study explains procrastination by revealing how perceived aversiveness from a delayed task and expected outcome utility generate asymmetric decisions between the present and the future. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
Article
Procrastination refers to an irrationally delay for intended courses of action despite of anticipating a negative consequence due to this delay. Previous studies tried to reveal the neural substrates of procrastination in terms of connectome-based biomarkers. Based on this, we proposed a unified triple brain network model for procrastination and pinpointed out what challenges we are facing in understanding neural mechanism of procrastination. Specifically, based on neuroanatomical features, the unified triple brain network model proposed that connectome-based underpinning of procrastination could be ascribed to the abnormalities of self-control network (i.e., dorsolateral prefrontal cortex, DLPFC), emotion-regulation network (i.e., orbital frontal cortex, OFC), and episodic prospection network (i.e., para-hippocampus cortex, PHC). Moreover, based on the brain functional features, procrastination had been attributed to disruptive neural circuits on FPN (frontoparietal network)-SCN (subcortical network) and FPN-SAN (salience network), which led us to hypothesize the crucial roles of interplay between these networks on procrastination in unified triple brain network model. Despite of these findings, poor interpretability and computational model limited further understanding for procrastination from theoretical and neural perspectives. On balance, the current study provided an overview to show current progress on the connectome-based biomarkers for procrastination, and proposed the integrative neurocognitive model of procrastination.
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Procrastination adversely affects individual’s learning, working, health, and well-being, which troubles many people around the world. Previous studies have indicated that people with higher achievement motivation tend to have less procrastination. However, how achievement motivation is linked with procrastination at the neural level is still poorly understood. Here, we adopted the voxel-based morphometry (VBM) and resting-state functional connectivity (RSFC) methods to study this issue. The VBM analysis revealed that higher achievement motivation was correlated with larger gray matter volumes in left precuneus (lPre). Furthermore, the RSFC results showed that the functional connectivity between lPre and right anterior cingulate cortex (rACC) was positively associated with achievement motivation and negatively correlated with procrastination. More importantly, a mediation analysis demonstrated that achievement motivation fully mediated the relation between lPre–rACC connectivity and procrastination. These findings suggested that lPre–rACC coupling might be the neural correlate underlying the association between achievement motivation and procrastination.
Article
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Previous research pertaining to job performance and voluntary turnover has been guided by 2 distinct theoretical perspectives. First, the push-pull model proposes that there is a quadratic or curvilinear relationship existing between these 2 variables. Second, the unfolding model of turnover posits that turnover is a dynamic process and that a downward performance change may increase the likelihood of organizational separation. Drawing on decision theory, we propose and test an integrative framework. This approach incorporates both of these earlier models. Specifically, we argue that individuals are most likely to voluntarily exist when they are below-average performers who are also experiencing a downward performance change. Furthermore, the interaction between this downward change and performance partially accounts for the curvilinear relationship proposed by the push-pull model. Findings from a longitudinal study supported this integrative theory.
Article
Presents an integrative theoretical framework to explain and to predict psychological changes achieved by different modes of treatment. This theory states that psychological procedures, whatever their form, alter the level and strength of self-efficacy. It is hypothesized that expectations of personal efficacy determine whether coping behavior will be initiated, how much effort will be expended, and how long it will be sustained in the face of obstacles and aversive experiences. Persistence in activities that are subjectively threatening but in fact relatively safe produces, through experiences of mastery, further enhancement of self-efficacy and corresponding reductions in defensive behavior. In the proposed model, expectations of personal efficacy are derived from 4 principal sources of information: performance accomplishments, vicarious experience, verbal persuasion, and physiological states. Factors influencing the cognitive processing of efficacy information arise from enactive, vicarious, exhortative, and emotive sources. The differential power of diverse therapeutic procedures is analyzed in terms of the postulated cognitive mechanism of operation. Findings are reported from microanalyses of enactive, vicarious, and emotive modes of treatment that support the hypothesized relationship between perceived self-efficacy and behavioral changes. (21/2 p ref)
Book
I: Background.- 1. An Introduction.- 2. Conceptualizations of Intrinsic Motivation and Self-Determination.- II: Self-Determination Theory.- 3. Cognitive Evaluation Theory: Perceived Causality and Perceived Competence.- 4. Cognitive Evaluation Theory: Interpersonal Communication and Intrapersonal Regulation.- 5. Toward an Organismic Integration Theory: Motivation and Development.- 6. Causality Orientations Theory: Personality Influences on Motivation.- III: Alternative Approaches.- 7. Operant and Attributional Theories.- 8. Information-Processing Theories.- IV: Applications and Implications.- 9. Education.- 10. Psychotherapy.- 11. Work.- 12. Sports.- References.- Author Index.
Book
This book outlines the creative process of making environmental management decisions using the approach called Structured Decision Making. It is a short introductory guide to this popular form of decision making and is aimed at environmental managers and scientists. This is a distinctly pragmatic label given to ways for helping individuals and groups think through tough multidimensional choices characterized by uncertain science, diverse stakeholders, and difficult tradeoffs. This is the everyday reality of environmental management, yet many important decisions currently are made on an ad hoc basis that lacks a solid value-based foundation, ignores key information, and results in selection of an inferior alternative. Making progress - in a way that is rigorous, inclusive, defensible and transparent - requires combining analytical methods drawn from the decision sciences and applied ecology with deliberative insights from cognitive psychology, facilitation and negotiation. The authors review key methods and discuss case-study examples based in their experiences in communities, boardrooms, and stakeholder meetings. The goal of this book is to lay out a compelling guide that will change how you think about making environmental decisions. © 2012 by R. Gregory, L. Failing, M. Harstone, G. Long, T. McDaniels, and D. Ohlson. All rights reserved.