The building blocks of

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The Building Blocks of Motivation: Goal Phase System

Cite as: Steel, P. & Weinhardt, J. (in press). The building blocks of motivation. N. Anderson, D. S. Ones, H. K. Sinangil, & C. Viswesveran (Eds.), Handbook of industrial, work & organizational psychology (2nd ed., Vol. 3). Thousand Oaks, CA: Sage.

2 Biographies Dr. Piers Steel is a professor in the human resources and organizational dynamics area and is the Distinguished Research Chair in Advanced Business Leadership at the Canadian Centre for Advanced Leadership in Business at the Haskayne School of Business, University of Calgary. Piers’ particular areas of research interest include meta-analysis methodology, personnel selection, culture and motivation, especially procrastination. He is the recipient of several international rewards, including the Raymond A. Katzell Award in I-O Psychology and the George A. Miller Award, given to the best article in psychology in the preceding five years. His work has been published in the premier journals in the social sciences, including, Journal of Personality and Social Psychology, Psychological Bulletin, Journal of Management, Journal of Applied Psychology, Personnel Psychology and Academy of Management Review. Dr. Justin Weinhardt is an assistant professor in the human resources and organizational dynamics area at the Haskayne School of Business, University of Calgary. Justin’s particular areas of research interest include computational modeling, dynamic motivation and decision making, multilevel theory and ethics. His research has been published in premier journals such as Journal of Applied Psychology, Journal of Management, Organizational Behavior and Human Decision Processes, and Organizational Research Methods.

3 Abstract 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.

Keywords: motivation, integration, goal phases, computational modeling

4 The Building Blocks of Motivation: Goal Phase System Motivation all begins with a goal. To be motivated, 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 effectively random. Motivation gives directions to our actions. While this direction is inherently important, a series of researchers make the case that motivation will become among the most important fields of social science. As civilization advances and prosperity becomes widespread, we remove most of the external maladies that were once major contributors 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 artificial 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 necessarily 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 motivational 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

5 environment that is more hostile to human rationality.” We need conscious, rational guidance, which must be based on a firm understanding of our motivational foundation. Unfortunately, the importance of motivation has led to an unwieldy number of theories on the topic, making understanding or advancement difficult. For example, Zeinder, Boekaerts, and Pintrich (2000) note, “the fragmentation and disparate, but overlapping, lines of research within the self-regulation domain have made any attempt at furthering our knowledge an arduous task” (p. 753). Dieffendorf and Lord (2008) express a similar sentiment, “the most important future development in self-regulation research will involve integrating these approaches so as to develop a more 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 mechanisms are relatively simple and few in number. Chaos theory has shown that considerable system complexity can arise from rather simple underpinnings (e.g., Mandelbrot sets). Follow up on this insight, Navarro and Arrieta (2010), while examining the chaotic nature of work motivation, concluded 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.

6 While three variables might be an overly aggressive reduction, our goal here is not to catalogue all previous motivational perspectives, but to focus on these likely motivational fundamentals. We start with temporal motivation theory (Steel & König, 2007), 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 examination of goal phases, which indicates fundamentally different formulations may be required to map different steps in the motivational process (Gollwitzer, 1990). Finally, we incorporate cybernetic or control theories of motivation, particularly the computational theory of multiple-goal pursuit (Vancouver, Weinhardt, & Schmidt, 2010; Vancouver, Weinhardt, & Vigo, 2014), which was specifically designed “to continue the process of integrating motivational theories” (Vancouver et al., 2010; p. 1002). We consider each in turn as well combine them, calling the resulting framework the Goal Phase System (GPS). It has three major phases (Goal Choice, Goal Planning, and Goal Striving), in which motivation unfolds differentially across each stage. 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. 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 excellent example of consilience (Wilson, 1998), a strong form of scientific proof. This was Steel and König’s (2007) 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 behaviorism), expectancy × value formulations (with origins from

7 economics), cumulative prospect theory (with origins in psychophysics), and need theory (with origins in personality research), they found several reoccurring features (Hodgkinson & Healy, 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 incorporate time into our motivational models, something that field had been lacking (e.g., Fried & Slowick, 2004; Locke & Latham, 2004; Mitchell & James, 2001; Schmidt et al., 2013). When one is evaluating among multiple possible outcomes for an act, some positive and others negative, a more complete but also more complex version of temporal motivation theory is recommended, which Steel and König (2007) cover in detail. For the purposes of this review, we will focus on the simplified form of temporal motivation theory, where people are evaluating one possible outcome instead a multiplicity of them, which consequently does not require modelling different functions for losses versus gains. Before considering each component in detail, we briefly review the overall equation: 𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀 =

𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸 × 𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉 1 + 𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼 × 𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷

Expectancy occurs in each theory, except in some forms of matching law (e.g., Mazur, 1987). It represents the perceived probability that an outcome will occur, dependent on both the situation and individual differences. Trait pessimism and challenging obstacles, for instance, will drive expectancy 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 temporal discounting, which

8 appears in hyperbolic discounting, behaviorism and need theory (i.e., press). Being on the bottom of equation, as their combined effects increase, motivation decreases. There are three components of temporal discounting. 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 temporal 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 particularly useful for modeling “the motivating power of approaching deadlines” (p. 550), especially procrastination. 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 theories of perfectionism or anxiety being the primary source of irrational delay (e.g., Pychyl & Flett, 2012), it is largely a function of temporal discounting. Gustavson, Miyake, Hewitt and Friedman (2014), using twin research to test temporal motivation theory, found 100% of the genotypic variance in procrastination was due to impulsiveness. Procrastination arises when we make long-term plans of actions, such as to start saving or writing next month. Since all possible actions for next month are similarly discounted, delay has minimal impact and goal choices are primarily made by expectancy and

9 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 temptation and task have imminent consequences, that we become fully engaged with the work at hand. Have given an overview of temporal motivation theory, we proceed to examine each component: Expectancy, Value, Impulsiveness, and Approach versus Avoidance Orientation. In keeping 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 often described as the belief of some outcome occurring based on some action (Lewin, 1951; Tolman, 1938). Regarding work motivation, expectancy played an essential role in early theories (Vroom, 1964) and still plays an indispensable part in current theories of work motivation (e.g., Vancouver et al., 2010, 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

10 (Carver & Scheier, 1998), optimism bias (Sharot, 2011) and risk seeking or aversion (Zuckerman, 2007). The field of work motivation has largely overlooked the multitude of concepts relating to expectancy. 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 motivation (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 is called (Yeo & Neal, 2006). As we will explore in more detail during subsequent sections, expectancy (and related concepts) have different effects depending on the goal phase (Vancouver, More, & Yoder, 2008). Generally, expectancy has a positive 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 rewarding, or reinforcing. For example, Rescher (1982) summarized philosophical and social science considerable attempts at providing a coherent 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. 5) concisely 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

11 (2011), when reviewing economic utility, 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 stimuli 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 indispensable. 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 mean, 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 difference between the two is not always readily apparent. 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

12 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 distinction 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 examined the interrelationship among intrinsic motivation, 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 performance 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 activity). 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 indirectly tied to performance, intrinsic motivation was an important determinant predictor of performance. This result supports motivational crowding (Frey & Jegen, 2001), where extrinsic incentives can overshadow other motivational forces. Finally, intrinsic motivation was a better predictor of quality performance, whereas incentives were a better 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).

13 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 Murrays’ (1938) catalogue 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 forthcoming (Winter, 1996). For example, Deshon and Gillespie (2005) consider fundamental categories of goal content, the “why” of motivation, are to address: agency (power), esteem (achievement) and affiliation. Still, these three primary needs are not an exhaustive list and there are other 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 motivator 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 balance between “the passions” and “the impartial spectator,” where the latter tries to moderate the former’s excesses

14 of lust, hunger, and anger (Ashraf, Camerer & Loewenstein, 2005). And Sigmund Freud continued Plato’s equestrian analogy 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 terminology 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, impulsive versus reflective, viscerogenic versus psychogenic, 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 accurate 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 heighten 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

15 emotions) tends to be stronger and quicker than System 2. Gifford (2002) provides a particular good account of the resulting conflict (p. 129): It is this divergence between the cultural and biological rates of time preference that creates a potential internal nature versus nurture conflict leading to self-control problems [like procrastination]. 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 individual’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 product 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 impulsive. 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 prefrontal Will vs. Resourcelessness. The work motivational 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 distinct clustering of related traits: trait procrastination, weak impulse control, lack of persistence, lack of work discipline, lack of time management skill, and the inability to work methodically” (p. 8). Given the number of work studies that include conscientiousness, where

16 many of these impulsive related facets are nested, particularly those Will vs. Resourcelessness related, the field has indeed being 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 considerably 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 supported 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 avoidance dichotomy means that expectancy, value and impulsiveness will be modified depending upon whether we have framed our goal as something 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 personality 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 versus avoidance mindset.

17 Consistent with prospect theory, a threat of a loss is considered more motivating 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 adverse. For example, two large natural field experiments 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), contestants 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, contestants 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 neurobiology of decision making, sometimes termed neuroeconomics, 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 extend to impulsiveness as well, though Steel and König (2006) leave the matter somewhat open: “Differences between positive and negative impulsiveness have not yet been definitively established, although they do appear to differ” (p. 898). Since this 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

18 impulsiveness can be further split in 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 motivating 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 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 commitment 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% holding 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, Lowenstein & 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 circumstance remaining ostensibly the same. Clearly,

19 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, stretching back to Lewin, Dumbo, Festinger and Sears (1944). Lewin et al. posit two stages: goal setting, where we deliberate, wish or establish what goals we will pursue, and then goal striving, during which we pursue them. Over the subsequent decades, every model contains at least these two, and at times nothing more. For example, regulatory 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 essentially the same two: goal choice and goal pursuit or striving. During goal choice, we decide which goal we will allocate resources. During goal striving, 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, what we want to identify goal phases where there are fundamentally different principals operating. Gollwitzer (1990), whose work in this is 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 different mindset, which is potentially a way of evaluating motivationally relevant criteria.

20 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 becomes unclear (Diefendorff & Lord, 2008). Functionally, we must evaluate our goals to know we have completed them, but the motivational properties appear to be largely the same as goal choice. Gollwitzer (2012), for example, now almost exclusively focuses on the qualitative 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 “reflective” mind, which is supervisory in nature, involving simulating possible futures and determining goal priority and regulation. Finally, Vancouver and colleagues (2008) have broken down motivation into choice, planning and striving, finding that expectancy and value demonstrate disparate findings across the motivational process.

21 Based on this work, GPS incorporates a three-stage framework of motivation: Goal choice, goal planning, and goal striving. As simplified in Figure 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 sub-goals, each of these stages may be revisited many times (Bateman & Barry, 2012). For example, 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 situation motivational perspectives, “Attempts to stay within the historical or prototypical boundaries of personality and social psychology are doomed to either distort those boundaries beyond recognition, 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 theories and their derivations, such as Vroom’s VIE theory or Fishbein and Ajzen’s (1975) theory of reasoned action, should show their highest verisimilitude. There is little disagreement on this point. Economists, management researchers, philosophers 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;

22 Vancouver, 2008; Vroom, 1964). If an individual is faced with a decision between choosing between goals, they are more likely to choose the option with 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 representative of the rational model and given the copious critiques of classical economics (e.g., Camerer & Fehr, 2006; Hodgson, 2011; Kahneman, 2003), which has doubled-down on rationality, it is important not to overreach. At all times, we are best described as quasi-rational (Thaler, 1994), meaning there are several caveats. Many of these deviations 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 that the potential gain of the same amount. As discussed, we have a motivational 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 disappointing base rates, we get married, start businesses, and adopt risky lifestyles. When asked

23 about our chances of divorce, bankruptcy or health complications, we underestimate. McKay and Dennett (2009) discuss how this these optimistic 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 evolutionary 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 selflimiting. 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 unbeneficial. As Sharot (2011) concludes, while “classic theories in economics and psychology assert that correct beliefs will maximize reward and minimize loss, many sources of evidence point to the conclusion that optimism is nonetheless advantageous compared to unbiased predictions” (p. 944). Returning to Johnson and Fowler (2011), inflated beliefs can still be handy when there is competition over contested resources. For example, in academia, the top journals in our field all have an acceptance rate of 5% or lower. However, submissions to the journals for the year are often above 1,000. This high submission rate appears inconsistent 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 different situations. Then, there is the issue of time. While expectancy × value models rarely include time as a variable (Locke & Latham 2004; Sonnentag, 2012), when they do, they typically incorporate an exponential function and a low discount rate, which is consistent with the rational model. However, hyperbolic discounting, as used in temporal motivation theory, and a high discount

24 rate tends to fit our choices better, reflecting the evolutionary kluge composed of the limbic and the prefrontal cortex (Fundenberg & 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 impulsiveness or delay becomes a constant and can be dropped from the equation. It is important to stress that it is not the absolute different in time between to 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 monumental. 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 expectancy. For example, the odds of an American citizen dying by a terrorist, 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 & Steward, 2011).

25 Aside from these four general trends, the judgment and decision-making field has a litany of other biases or “anomalies” that affect how we estimate expectancy and value during goal choice. Bazerman and Moore (2013) organize these around three heuristics: Availability, Representativeness and Confirmation. For example, 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 theory, are approximations and provide only a reasonable account for most goal choice situations. Such discrepancy is normal, however, and not necessarily 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) paraphrasing 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 conserve 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. 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 lustre 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)

26 meta-analysis on the topic. Attempts to test expectancy × value theories found that the components predicted better when allowed to operate separately rather than multiplicatively. 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 direction: expectancy. We know that from work on the planning fallacy and the illusory superiority effect that individuals are poor planners (Beuhler, Griffin, & Ross, 1994; Kahneman & Lovallo, 1991; 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 fallacy, 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.

27 Although, the majority of research and theory regarding expectancy has focused on the positive effect, Bandura (1997) proposed that in a preparatory context that self-efficacy might be negatively related to performance, implying that self-doubt might be a good thing. For example, when discussing 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 preparatory practice. Some uncertainty clearly benefits 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 preparatory 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 matter” (p. 76). In a classroom setting, where individuals 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 individuals’ 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 taking 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 2

28 depicts this relationship. Kukla (1972) first discussed the non-monotonic, discontinuous relationship and later Carver and Scheier (1998) integrated it into their self-regulation 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 effectively abandon it. However, if the individual believes the task is somewhat achievable, but still difficult, they will plan to allocate considerable resources towards realizing it. Likewise, as perceived ability (i.e., expectancy increases), they will plan to allocate fewer resources. In the same way we are often cognitive misers, using the shortcut of heuristics, we are also motivational misers, not wanting to spend more energy and time on a task than necessary. Vancouver et al. (2008) provided 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, & Weinhardt (2014) find support for the latter by examining the discontinuous nonmonotonic model (Figure 2) in regards to value. Across two studies, they found that value increased planned resource allocation. As can be seen in Figure 2, an increases 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 influenced by affective forecasting errors regarding the capabilities and desires of our future selves (O’Donoghue & Rabin, 2008; Wilson & Gilbert, 2005).

29 Where expectancy and value may bias our planning, impulsiveness can make planning disappear. Overall, individuals who are high on impulsiveness 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 impulse purchase (Kalla & Arora, 2011), as there is effectively no time between choice and implementation. Why does this impulsiveness do this do 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 better 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 neuroscience to organizational science is still underdeveloped itself, though there are advocates (e.g., Volk & Kohler, 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 around maximizing hits and avoiding misses while a prevention focus should pursue goals constructed around ensuring correct rejections and

30 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 emphasize 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 individuals 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 temptation. 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 (Nordren, van Harreveld, & van der Plight, 2009). Motivation during Goal Striving Similar to the goal planning, expectancy × value theories fail to fully translate to goal striving phase of GPS. For example, Klinger (1977), in early work notes, “expectancy × value theories have been only very modestly successful in predicting vital aspects of goal striving, such as work and quality of performance” (p. 329-330). Where during goal planning, it was

31 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, 2013), 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 competing choices and more fully engage in singular goal pursuit. However, during goal striving, excessive expectancy can work against timely goal completion in multiple ways. During goal choice, we are indifferent to where the source of our expectancy comes from. If the outcome is sufficiently likely, and the reward competitively desirably, we will make pursuit intentions. During goal striving, 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

32 are not met, people 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 selfregulation. It would have been better if they started with a more modest outlook (Aspinwall, 2005). To prevent this, expectancy has to be crafted in a particular way, contingent on hard work or perseverance. In other words, expectancy should not be centered on the outcome being likely, which sufficed during goal choice, but that despite inevitable and repeated obstacles and setbacks, we can be confident that we have the internal resources and depth of character to meet and overcome them (Aspin & Taylor, 1997; Baumeister, Heatherton & Tice, 1994; Schwarzer, 2008). Success will come, but after sometimes considerable 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 argues extrinsic rewards can crowd out or replacement 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 a goal is not necessarily what propels us towards achieving it. As reviewed by Steel and MacDonnell (2012), 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 extrinsic rewards can be a form of positive fantasy, which can be likened to motivational pornography (Kappes & Oettingen, 2011). Fantasizing diminishes motivational

33 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 cognitive capacity. For any cognitively complex task, emphasizing the rewards at completion is experienced 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 definitely in standing. It helps explain the importance of intrinsic motivators. While they may be small compared to extrinsic motivators emphasized during goal choice, they are experience during the act of goal pursuit and benefit from our impulsive nature. Being immediately generated and consumed, these intrinsic rewards are usually highly valued. Impulsiveness and delay also largely accounts for intention-action gaps and procrastination (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 previous phases, the time until implementation may have been distant enough that temporal discounting 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 outcomes become

34 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 framing goals with an approach orientation (Elliot & Friedman, 2006; Howell & Watson, 2007; Schnieder, 2001). 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 doing something). 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 behaviors that take 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 willpower, 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 beginning to emerge in organizational psychology, we provide an overview of the issues

35 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 particularly evident when we attempt to deploy cognitive resources among a number of tasks simultaneous (Evans, 2008; Miller 1956; Simon, 1955). As a result, when individuals multi-task, they experience fatigue and performance deficiencies (Baumeister, Vohs, & Tice, 2007; Kurzban, Duckworth, Kable, & Myers, 2014), though certain tasks may exacerbate theses effects more than 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? Galliot 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 Galliot et al. (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 the Galliot et al.’s investigations. Job, Walton, Bernecker, and Dweck (2013), replicating their earlier work, confirmed that subsequent performance only decreased for people who believe self-control is a limited 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 individuals are given the opportunity to adapt to their situation, depleting effects will disappear. Because of these

36 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 Kurban et al. (2014). We believe the model by Kurzban et al. (2014) provides the best motivational account for understanding 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 mental 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. (2010) who proposed that expectancy and value are changing dynamically as individuals strive for multiple competing goals. Building on this model by incorporating impulsiveness, individuals who are high impulsiveness may find striving towards their long-term goals more fatiguing and effortful because alternative 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, motivational theories and research designs are largely snapshots of dynamic phenomena

37 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 answered by researchers examining work behavior 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 intimate relationship between statistical tools, empirical 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) researchers use to account for some phenomenon become our theories for that phenomenon. He calls this the tools-to-theories-heuristic. There are numerous 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 tools also shape how we think about psychological phenomenon. For example, he discusses how signal detection theory is theoretically grounded in Neyman-Pearson hypothesis testing. Signal detection theory was based on new tools (statistics) not widely used within the field at the time and therefore required new data (Tanner, & Swets, 1954). Thus, data did not drive the theory, but rather the tools and the

38 theory required new data. In the organizational literature, with the increase use of multilevel 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 influence 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 advantages 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 descriptions 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 representation 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 theories, there is little ambiguity regarding the theory. And through our own mental modeling we may believe we can easily understand dynamic phenomenon, 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). Necessarily then, as our field begins to develop dynamic theories and use dynamic empirical

39 designs, computational 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., 2010), formally describing and testing parts of an existing present theory (Vancouver, Putka, & Scherbaum, 2005), resolving conflicting theoretical issues (Vancouver & Scherbaum, 2008; Vancouver & Weinhardt, 2012), integrating theories (Steel & König, 2006; Vancouver et al., 2014), and resolving conflicting empirical findings (Vancouver, Tamanini, & Yoder 2010). 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 computational models, we will turn to computational models regarding motivation. While the 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., 2010; 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 discussing each model in detail, we focus on computational 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;

40 Schmidt, Dolis, & Toli, 2009), the field is rather limited in regards to research on multiple-goal motivation. To rectify this, Vancouver et al. (2010) 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 accomplished more, they have higher expectancy that the goal will be reached. Meanwhile, their expectancy 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 deadline approaches. Near the deadline, assisted by hyperbolic discounting, it becomes clear that it is difficult to achieve both goals, whereupon expectancy dominants 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 incorporate different motivational components and formulations. Vancouver et al. (2010) 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 continuously monitored the time until deadline. They also modeled people’s impulsiveness, their sensitivity to deadlines. Individuals who are more sensitive 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 integrating cognitive psychology theories of learning with theories of

41 motivation. In this expansion, biases in time perception were incorporated and how they may arise as individuals regulate multiple competing 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 regulation, 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 system, who propose that an affective selfregulatory 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 individual experiences negative affect, which signals the need for more resources to be allocated to achieving a goal. If goal progress is above a certain 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,

42 very little empirical 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 process model, which examines several interconnected 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) computational modeled the regulation of wellbeing and stress using the same self-regulatory framework found in theories of motivation. Also, researchers often discuss the outcome of emotional labor as emotional exhaustion, which is the same nomological net of the “depletion” effects described by selfcontrol researchers (Beal et al., 2005). Consequently, computational models should help integrate the work on emotional regulation and labor with motivation 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 it outcomes. Though there are constrained number of major components, they interact and change over time, making computational models an especially useful tool for capturing motivational dynamics. If we want the field of work motivation to be considered in the same conversation as other sciences, it appears that we need to develop and test our theories

43 computationally (Harrisson Lin, Carroll, Carley, 2007; Ilgen & Hulin, 2000; Vancouver & Weinhardt, 2012; Weinhardt & Vancouver, 2012). Motivational Interventions In our efforts to improve motivation, there generally has been little understanding of the complete 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 expectancy × value model operate best at determining goal choice and are less appropriate for subsequent 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 implemented compared with the underlying motivational 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 significantly 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, “longterm incentives are not an efficient way of motivating senior executives, irrespective of national culture” (Pepper & Gore, 2014, p. 26). To effectively apply GPS, we need to take a more sophisticated approach to motivational interventions. To do this, we need to understand where motivational interventions are best applied by connecting them to the appropriate goal phase and subcomponent as well as

44 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 customizing interventions to the individual” (p. 410). They recommend developing diagnostic procedures, such as the Motivational Diagnostic Test (Steel, 2011), that identify people’s particular motivational 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 motivational 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 motivational elements of impulse control and self-confidence (Clarke, 2006; Goleman, 1998). Almost regardless of a self-regulatory intervention’s espoused heritage, the core and most effective features can be largely classified as operating on a particular goal phase and motivational component (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 comes 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

45 “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 bicamerialism, 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 decisionmaking (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 assist in making decisions more consistent with our values, even in situations of considerable complexity. 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

46 multi-attribute utility theory, structured 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 applications exist for more pivotal and established life events, such as choice of pension plan, with adoption further eased, as the International Society on Multiple Criteria Decision Making documents, through dozens of decision-making software programs. Through a combination of these two factors, 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 compensate for them with explicit external processes. Roy et al. propose a method that improves estimation by keeping strict objective records of how long projects take and relying on these to determine 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 overcommitments 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 scheduling, however, where we just rationally allocate time and resources so we can tackle the task at hand. It also has meta-cognitive aspects,

47 where we anticipate our motivational weaknesses and plan now to overcome them (Gollwitzer & Oettingen, 2011). Unfortunately, these same weaknesses reduce our capacity to plan. Those overconfident 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 selfregulation aren’t likely to self-regulate. And, while there are many techniques available to reduce impulsivity, 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 or 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 temporal motivation theory (Gröpel & Steel, 2008). It counsels us to make goals that: we are committed 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 performance. 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 moderates one motivational technique during goal planning. If there is sufficient goal commitment, goal planning is further assisted by mental contrasting (Kappes, Singmann, &

48 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 it accompanied challenges and obstacles. If commitment remains strong, planning typically ensues. The false hope syndrome can also be mitigated during goal planning. 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 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 prefrontal 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 system 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 chapter 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 reevaluating goal choice, helping to ensure that goal striving 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 impulsiveness is to avoid making decisions altogether. With 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

49 cultivated to prevent impulsive choice, enabling us to persevere towards our goals instead of succumbing to temptation (Baumeister, Muraven & Tice, 2000). Implementation intentions 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 components of motivation, used together they are better 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 drawing on attribution theory and explanatory style. The first is learned optimism (Seligman, 2011), where we improve expectancy in the face of failure by attributing setbacks to temporary, situation specific and external forces (rather than permanent, 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

50 striving, intrinsic motivation is enhanced. These feelings are influenced by our general causal attributional style, so we can improve our intrinsic motivation by emphasizing volition, even if 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 labelled 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 fundamental is this technique, the foundational research done in this area is with children and chimpanzees. Mischel and Baker (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, temptation are framed in terms of their abstract and symbolic features, such as Mischel having children focus on pretzel’s 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 representations, 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. 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 choose 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 Dean’s words, this symbolic representation is vital for self-control, for without it “being completely focused on what

51 they want, they seem unable to stand back from the situation, so to speak, and subjugate their desire to the pragmatic context.” 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). Summary 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 simplistic 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 complicated 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 strategically, balancing parsimony with completeness. As Einstein suggested “Make everything as simple as possible, but not simpler.” When drawing 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

52 participant group that is selected for a precise motivational weakness, such as lower selfconfidence or impulsiveness. Our motivational interventions should be appropriately targeted to their motivational weaknesses, a matching 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 motivational 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 decision-making that Heath and all the others warned us about. We can learn self-regulatory techniques, create motivational schemes and build environments that coax us towards better goal choices, better goal plans, and superior goal striving.

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77 Figure 1. Goal Phase System

• Expectancy (+) • Value (+) • Impulsiveness (+)

Goal Choice

Goal Planning • Expectancy (-) • Value (+) • Impulsiveness (-)

• Expectancy (+/-) • Value (+/-) • Impulsiveness (-)

Goal Striving

Note. The approximate effect of the building blocks of motivation during each goal phase.

78 Figure 2. The Relationship Between Expectancy and Value on Motivation