Cognition, Motivation, and Lifespan Development

Chapter 7

Cognition, Motivation, and Lifespan Development Margaret E. Beier, Brittany C. Bradford, W. Jackeline Torres, Amy Shaw and Michelle H. Kim Rice University, Houston, TX, United States

Prior to the 1960s, research in organizational science focused almost exclusively on ability determinants of performance (Kanfer, 1990), perhaps as a function of the consistent validity coefficients associated with measures of ability (Hunter & Hunter, 1984). Nonetheless, organizational scientists and laypeople alike have recognized the importance of both abilities and motivation as determinants of learning and performance (Kanfer, 1990; Mitchell, 1982). In addition to the opportunity to perform a job in the first place (Blumberg & Pringle, 1982), the two essential components of job performance are cognitive ability and motivation. Indeed, researchers have derived the formula f(performance) 5 motivation  ability to denote this relatively simple idea (Mitchell, 1982). Little has changed about this performance formula over time, even with the proliferation of constructs in organizational science and increasingly sophisticated research methods. That is, organizational scientists, consultants, managers, and supervisors alike understand that the best workers are those who have the ability to do the job (the “can do” factor), and the motivation to do the job and to do it well (the “will do” factor). Given the seemingly universal understanding of the importance of ability and motivation for performance, it is perhaps surprising that sparse research examining performance actually includes the study of ability, motivation, and their interaction. Indeed, motivational theories that describe the process of goal setting and goal striving rarely describe how differences in abilities might impact personal goals and achievement (Schmidt, Beck, & Gillespie, 2013). Similarly, theories of intelligence that describe the importance of

Work Across the Lifespan. DOI: https://doi.org/10.1016/B978-0-12-812756-8.00007-4 © 2019 Elsevier Inc. All rights reserved.

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cognitive abilities for novel problem solving and daily performance generally neglect the role of volition and perseverance (Salthouse, 2010; Schneider & Newman, 2015). Although some researchers have successfully integrated motivation and intelligence theories (Kanfer & Ackerman, 1989, 2004), further integration of these constructs represents a growth opportunity in organizational science. The consideration of abilities and motivation in concert is further complicated by the fact that abilities and motivation do not remain stable throughout the lifespan; rather, they are likely to change in important ways that affect continuous development and performance at work (Baltes & Baltes, 1990; Carstensen, Isaacowitz, & Charles, 1999; Cattell, 1987; Heckhausen & Schulz, 1995; Hertzog, Kramer, Wilson, & Lindenberger, 2008; Salthouse, 2010; Schaie, 2013). The purpose of this chapter is to examine the cognitive and motivational determinants of performance in the context of lifespan changes. We are not the first to examine these relationships. Decades ago, Kanfer and Ackerman (1989) explained the importance of integrating abilities and motivation theory to understand workplace motivation. Here we review this seminal paper and expand the consideration of abilities and motivation for working across the lifespan.

WORKPLACE PERFORMANCE DOMAINS OF INTEREST This chapter’s goal is to understand workplace behavior with a focus on the extent to which cognition and motivation affect engagement and performance in jobs and training and development activities. Within this context, we consider task and contextual performance (Borman & Motowidlo, 1993), and formal training and informal development opportunities (Beier, Torres, & Gilberto, 2017). Task performance includes those elements of job performance that either transform raw materials into goods and services (e.g., selling products, working on an assembly line, teaching) or activities that service the core functions of organizations (e.g., computer technician, human resources, staffing). Contextual performance represents behavior that contributes to the psychological, social, and organizational context of work, such as promoting trust and positive affect among members of a work team or defusing a conflict with a supervisor or coworker (Motowidlo & Kell, 2013).

AGING AND COGNITIVE ABILITIES A comprehensive review of theories of cognitive abilities is beyond the scope of this chapter (see Drasgow, 2013; Reeve, Scherbaum, & Goldstein, 2015; Schneider & Newman, 2015; Chapter 2 of this volume). The two ability constructs most relevant to our discussion, because they are related to job demands for most jobs, are fluid and crystallized abilities, alternatively called reasoning and knowledge abilities, respectively (labels we use here to

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simplify the presentation; Salthouse, 2010). Examining lifespan changes in abilities requires consideration of the variability at which people age. At the between-person level, for instance, the ability profile of one 50-year-old may be similar to that of a 30-year-old, while another 50-year-old may have the ability profile of an 80-year-old (Hertzog et al., 2008). These betweenperson differences are a function of the idiosyncratic nature of human development; that is, they are a function of the variability in within-person changes in abilities with age. For instance, depending on a person’s life experiences, he or she may develop an array of skills through practice and may simultaneously lose others through decay. The end result of these within-person changes is that there is no one ability profile that corresponds to a specific age or age range. Rather, the focus in cognitive aging research is to describe normative changes in abilities over the lifespan, recognizing that each individual may reflect these normative trends to a different degree (Hertzog et al., 2008). Unfortunately, lifespan ability changes are typically thought of as monotonically declining functions. Indeed, there is some truth to the notion that abilities decline with age when the scope of abilities considered is narrow, such as the ability to solve novel problems (reasoning abilities), cognitive processing speed, or short-term memory abilities (e.g., working memory capacity; Salthouse, 1996). When the scope of considered abilities is broadened, however, the picture of abilities and aging becomes one that includes both decline and growth (Ackerman, 1996; Kanfer & Ackerman, 2004). Specifically, as people age and reasoning abilities tend to decline, knowledge abilities related to expertise and lived experiences tend to remain stable or increase (Salthouse, 2010). Reasoning and knowledge abilities are correlated in the general population; that is, people who have relatively higher standing on measures of reasoning ability are also likely to acquire more knowledge across the lifespan. This relationship reflects the idea that investing attention and effort to learn something will pay off in terms of knowledge development (Ackerman, 1996; Cattell, 1987). Moreover, investment theories of intelligence—theories that describe reasoning ability as an antecedent to knowledge development— help explain why general cognitive abilities are predictive of job performance. That is, general ability provides an index of a person’s potential to acquire job knowledge through job experience. Hunter (1986), for example, conducted a meta-analysis of military data and found that the effect of general ability on job performance was mediated largely by job knowledge. Furthermore, Schmidt and Hunter (1998) reviewed validity research and concluded that job knowledge can predict job performance at least as well as general mental ability—and that knowledge provides incremental validity above general ability. They remark, however, that job knowledge is not often used in personnel selection because it is generally considered unfair to give candidates job knowledge assessments in the absence of job experience.

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There are theoretical perspectives that consider knowledge a byproduct of intelligence, but not a central component of intelligence (Kail & Salthouse, 1994). Other theoretical models of intelligence do not consider knowledge at all, but focus instead on general mental ability, which resembles reasoning abilities in their generality (compared to the specificity of knowledge-type abilities; Ree, Earles, & Teachout, 1994; Spearman, 1904). Indeed, general mental ability models have greatly influenced the measurement of cognitive abilities in organizational science (particularly industrial and organizational psychology; Ree et al., 1994). This is unfortunate because general mental ability models paint a relatively stark picture of declining abilities with age. Moreover, this picture does not reflect the reality that people seem to continue to thrive throughout the working lifespan (Beier, Young, & Villado, 2018). For example, the average age of leaders in the majority of global organizations and societies is well beyond the age of declining reasoning abilities (i.e., early adulthood). Because general ability assessments rely heavily on decontextualized general cultural knowledge and reasoning abilities, they will tend to underestimate the potential of older workers if used in selection contexts. In summary, understanding adult intelligence requires moving beyond standard paradigms of general intelligence and intelligence assessment to include a broader array of knowledge acquired through work and educational experiences across the lifespan (Ackerman, 2007). Moreover, the serious scientific study of adult intellectual development requires the creation of knowledge assessments that can be used to predict outcomes related to success in adult domains (e.g., success at work, home, and leisure; Ackerman, 2007; Beier et al., 2018). These types of assessments would also be useful in selection contexts to ensure that older candidates get credit for their vast array of knowledge and expertise.

MOTIVATION A comprehensive review of lifespan motivation theory is outside of the scope of this chapter, but see chapters 4, 5, and 6 of this volume. Motivation is typically conceptualized as influencing the direction of behavior (i.e., what a person chooses to do, such as focus on the task at hand or chat with a coworker), the intensity of behavior (i.e., task effort), and the persistence of behavior (i.e., continuing to focus even when obstacles occur; Kanfer, 1990; Schmidt et al., 2013). Here we review theories of motivation and motivational processes most relevant to work across the lifespan: theories of goal choice and goal striving, particularly pertaining to self-efficacy and selfregulation (Bandura, 1991; Locke & Latham, 2002). Goal choice theories are dominated by expectancy-value models, in which individuals’ subjective expectations and subjective valuations of various expected outcomes predict the choice to follow a specific course of action. Vroom’s (1964) valence-instrumentality-expectancy model describes

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how individuals choose between tasks, jobs, and how much effort they decide to exert in order to achieve the highest subjective benefits. Valence refers to the value a person assigns to an outcome; instrumentality refers to perceptions of how completing any given action will be related to achieving a valued outcome; and expectancy refers to a person’s expectation that exerting effort toward the action will lead to its successful completion. After a goal has been selected, theories of goal striving highlight the role of expectancy and self-regulation directed toward accomplishing an outcome. Relevant constructs for goal striving tend to be self-regulatory processes related to behavioral intensity and persistence (Kanfer, 1990; Schmidt et al., 2013). Although these processes are often presented as distinct and linear (e.g., an individual chooses a goal and then regulates his or her attention until that goal is met), in reality goal choice and goal striving represent a recursive process in that during the process of goal striving a person may reevaluate his or her commitment to a goal, disengage, and reenter the goal choice without having achieved the desired outcome (Kanfer, 1990). Locke’s (1968) goal-setting model integrates goal choice and goal striving. In this theory, goals transform motivational states into action by directing attention, coordinating effort, increasing task persistence, and facilitating strategies for goal accomplishment. An important aspect of goal choice and goal striving in Locke’s (1968) approach is self-efficacy (Bandura, 1991), which is a person’s belief about his or her ability to attain a specific goal. Self-efficacy is expected to influence goal choice because it represents a person’s assessment of whether engaging in behavior associated with goal accomplishment will pay off in the end (i.e., expectancy; Vroom, 1964). Self-efficacy should also influence goal striving because it represents an important source of feedback during task performance that is an integral part of self-regulation (attentional focus and persistence) and, as such, is an important component of self-regulation (Bandura, 1991). Research on cognitive abilities has often been conducted separately from research on motivation. This is unfortunate, particularly in lifespan research, because changes in abilities will theoretically affect both goal choice and other motivational processes engaged during goal striving, such as self-efficacy and self-regulation (Schmidt et al., 2013; chapters 4, 5, and 6 of this volume). For instance, a manager may recognize that she is having difficulty remembering detailed budget information as she ages, causing her to spend more time reviewing reports for specific budget information before strategy meetings. She may even delegate the task of presenting at strategy meetings to a subordinate because she does not feel confident in her memory. That same manager, however, might feel confident about her ability to oversee a merger between two operational units given her extensive knowledge of the job tasks and roles involved. This example illustrates that for better and for worse, aging affects not only the cognitive resources that a person can bring to bear on a particular task in terms of memory and knowledge, but also task motivation.

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Theories of aging and motivation have recently been adapted to understand workplace motivation and aging (Carstensen et al., 1999; Heckhausen, Wrosch, & Schulz, 2010; Kanfer & Ackerman, 2004). These theories incorporate age-related changes in abilities and motivation to varying degrees and are described further below. Socioemotional selectivity. Socioemotional selectivity theory suggests that as people age, their focus shifts to time remaining in life and away from time since birth (Carstensen et al., 1999; Chapter 6 of this volume). This shift in time perception influences goal selection; in particular, as people age the goals they choose are predicted to be increasingly personally and emotionally rewarding (e.g., spending valuable time with loved ones) and less externally rewarding (e.g., compensation, prestige). One reason for this predicted shift is that older adults might perceive investments toward achievement goals to not be an optimal use of limited resources. How might time perspective manifest in the workplace? A systematic review on socioemotional theory in the workplace found that job satisfaction, organizational commitment, and attitude toward personal development were all positively correlated with perceptions of remaining time at work (Henry, Zacher, & Desmette, 2017). Further research on future time perspective has found that it has a mediating role in the negative relationship between age and the motivation to continue to engage in work (Kooij, Bal, & Kanfer, 2014). In Kooij et al.’s study, age was also found to be related positively to motivation for generative goals such as sharing expertise and helping others. In summary, research on socioemotional selectivity theory suggests that changes in motivation occur across the lifespan and that people make decisions about how to best invest their time and resources given perceptions of remaining time; older adults perceive limited time and thus should be more likely to seek emotionally fulfilling goals rather than achievement goals (Carstensen et al., 1999; Kooij et al., 2014; Stamov-Roßnagel & Biemann, 2012). Selection optimization and compensation. The model of selection, optimization, and compensation (Baltes & Baltes, 1990; Chapter 4 of this volume) identifies strategies used for goal setting and goal striving. Selection refers to the process of goal selection to reach a desired state; optimization refers to the process by which people use available resources (e.g., job skills) toward goal achievement; compensation refers to the process by which people address lack of resources by using alternative solutions. Resources for goal striving can include psychological (e.g., personality, interests, attitudes), physical (e.g., health), and social resources (e.g., family, community; Freund, 2008). Resources are not static, but are likely to decline (e.g., reasoning abilities) or increase (e.g., expertise) across the lifespan. Selection, optimization, and compensation strategies have been useful in explaining differences in perceptions of work ability (i.e., perceptions of the match between job demands and abilities; Ilmarinen & Ilmarinen, 2015)

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among older adults (Riedel, Mu¨ller, & Ebener, 2015; Chapter 4 of this volume). Given that work ability perceptions are related to the desire to continue to work versus retire (McGonagle, Fisher, Barnes-Farrell, & Grosch, 2015), this research suggests that selection, optimization, and compensation strategies can influence retirement decisions. Furthermore, the use of these strategies has been found to shield employees from work stress (e.g., problem solving demands; Schmitt, Zacher, & Frese, 2012) and to improve job performance (Yeung & Fung, 2009). In summary, the model of selection, optimization, and compensation addresses both goal choice and goal striving, proposes that older adults consider their available resources when setting and achieving goals, and proposes that older adults actively seek alternatives to address barriers to goal achievement. These alternative solutions may include using resources that are gained across the lifespan, such as personal resources (e.g., acquired job knowledge and skill expertise) or community resources (e.g., use of public transportation instead of driving one’s own vehicle). The selection, optimization, and compensation model puts forward the idea that people can and do actively seek strategies to address age-related changes in abilities while continuing to set and achieve goals throughout the lifespan.

INTEGRATING ABILITY AND MOTIVATION Kanfer and Ackerman’s (1989) seminal research integrated motivational theories of goal choice and goal striving (Bandura, 1989; Heckhausen et al., 2010; Locke & Latham, 2002; Vroom, 1964) with cognitive theories of skill acquisition (Anderson, 1982) and resource theories (Norman & Bobrow, 1975) to understand the motivational processes important during various phases of skill acquisition. The models presented and tested by Kanfer and Ackerman (1989) described early phases of skill acquisition as cognitively demanding such that they require intense attentional focus. Moreover, introducing goals during early phases of skill acquisition was detrimental to performance. It was only during later phases of skill acquisition, when attentional resources were freed up, that the introduction of goals positively impacted performance in the series of studies conducted by Kanfer and Ackerman (1989). Kanfer and Ackerman’s (1989) findings highlighted two important factors that impact skilled performance. The first is that the amount of effort a person expends on task performance is a function of both the person’s attentional capacity (i.e., cognitive abilities) and the complexity of the task he or she is performing. Although this idea is straightforward on its surface, it is often overlooked in laboratory studies that manipulate and examine task difficulty and generally treat individual differences in abilities and interest/ motivation as error variance. It is similarly overlooked by applied researchers interested in individual differences in performance, with less focus on the

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difficulty of the tasks in which people are engaged (Beier & Oswald, 2012; Cronbach, 1957). Resource theories examine task performance as a function of the skills and abilities an individual brings to task performance (the resources) and the demands of the task (Kanfer & Ackerman, 1989; Norman & Bobrow, 1975). Resources in this framework refer to the amount of attentional capacity a person has to devote to task performance. A process is considered resource limited when increased attentional effort results in better performance; for instance, when a learner engages in online training modules with focused attention to learn new project management software. Resource-limited tasks are also referred to as resource dependent because diverting attention from them will impair performance. By contrast, performance on data limited tasks is not a function of attentional effort directed at task performance. These tasks are insensitive to the amount of attentional effort—or resources—applied to their completion. In general, data limited tasks tend to be those that are either automated such that they can be performed with relatively little or no effort (e.g., typing and other manual labor activities), or they are so easy (simple arithmetic) or so difficult (thermal dynamics problems) that increasing attentional effort during task performance would not change the performance outcome for an average person (most would get the arithmetic right and the thermal dynamics problem wrong; Beier & Oswald, 2012; Norman & Bobrow, 1975). Notably, the thermal dynamics problem— while data limited for the majority of the population—would be resource limited for the nuclear physicist. Thus, a key consideration in resource theories is that task performance is a function of task difficulty and also the cognitive resources (attentional capacity and prior knowledge) possessed by the person performing the task. The second factor highlighted by Kanfer and Ackerman (1989) is that motivational processes such as goal setting and self-regulation during goal striving introduce cognitive load during task performance (i.e., they consume attentional resources). Thus, engaging in self-regulation in pursuit of a goal will serve to channel attentional resources away from task performance, reducing the resources available to devote to performance. This reduction of attentional resources would be detrimental to performance when tasks are resource limited, such as during early phases of skill acquisition, when learning a new task requires focused attentional effort (Ackerman, 1988). Indeed, Kanfer and Ackerman (1989) demonstrated over three lab experiments with over a thousand Air Force trainees that goals introduced early during skill acquisition could be detrimental to performance because early performance on novel tasks is resource limited. Goals introduced later during skill acquisition, however, were not detrimental, but beneficial to performance because task practice freed up cognitive resources for self-regulation directed at goal attainment (the task became less resource limited allowing the person to focus on goal attainment).

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The ideas presented in Kanfer and Ackerman’s (1989) research—particularly as related to integrating cognitive and motivational processes—were revisited in a second seminal review on motivational aging (Kanfer & Ackerman, 2004). Therein the aging process is described as importantly affecting resources, goals, and self-regulation implicated in task performance. Kanfer and Ackerman (2004) describe adult intellectual development as a function of decline, growth, reorganization, and exchange. Decline refers to declines in reasoning abilities, and growth refers to the increases in knowledge gained through educational and other experiences throughout the lifespan described above. Regardless of age, both reasoning and knowledge abilities affect cognitive resources available for task performance. Learning a completely novel task should be resource limited for everyone because it requires attentional focus before the new skill or knowledge is routinized (Ackerman, 2007). Learning novel tasks can be contrasted with expanding one’s repertoire of existing knowledge about an already well-learned domain, which may require relatively little attentional effort. Age is an important moderator of these relationships, however, because age affects the cognitive resources a person has available to complete the task at hand, affecting how resource or data limited that task is. Reorganization and exchange in Kanfer and Ackerman’s (2004) theory refer to shifts in the types of goals that adults are likely to pursue throughout the lifespan, and related changes in interests that emerge in adult development. Specifically, Kanfer and Ackerman (2004) describe workplace motivation related to three functions: an effort-performance function, a performance-utility function, and an effort-utility function (see also, Kanfer, 1987). Consideration of cognitive abilities is found in the effort component of these functions; that is, perceptions about the amount of effort required for adequate performance will be a function of a person’s perception of his or her reasoning and knowledge abilities, which should theoretically be based on actual ability levels. Effort-performance function. The effort-performance function describes the relationship between task performance relative to the amount of effort involved. Fig. 7.1 shows a series of possible effort-performance functions; it is similar to the function presented in Kanfer and Ackerman (2004), but focuses only on mature workers. Notably, the curves in Fig. 7.1 are relevant to real and perceived relationships between effort and performance given that both perceptions and objective assessment of effort necessary for task engagement will influence a person’s motivation for task involvement. The dashed line (balanced tasks) represents a relatively resource limited task; a task for which the worker perceives that increasing effort and attention will lead to increases in performance. For example, this curve could represent the perception of the amount of effort needed for different levels of performance on new project management software for an experienced project manager. The project manager has prior knowledge and reasoning ability to apply to

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FIGURE 7.1 Effort-performance function for mature workers. Chart shows tasks that are relatively balanced (dashed line), tasks that rely heavily on reasoning abilities (double line), and tasks that rely heavily on knowledge abilities (solid line). Figure adapted from Kanfer and Ackerman (2004), showing functions only for mature workers.

the task and as such, he or she should perceive that investment of effort would pay off. The other curves on the figure represent effort-performance functions for job tasks that vary in their reliance on knowledge or reasoning abilities (Kanfer & Ackerman, 2004). The solid line represents knowledge-based tasks; tasks that will be relatively data limited (or effort insensitive) for people who have existing knowledge or expertise. For these tasks, applying low-levels of effort will pay off in terms of performance, but because routines are well practiced and automated, additional effort will not tend to pay off in terms of increased performance (i.e., performance will reach an asymptotic level and remain at the same level regardless of additional effort expended; Ackerman, 1988). For instance, after submitting numerous expense reports through an online accounting system, the procedure should be relatively routinized and error free for most users. Applying increased mental effort will not improve performance. Indeed, no matter how much a person would like this process to take less time and effort, increasing attentional focus will likely not make it more efficient. In contrast to knowledge-based tasks, tasks that are heavily reliant on reasoning-based processes will be data limited throughout task engagement, not just at early stages of task engagement as are knowledge-based tasks. The hypothetical function for such tasks is shown in the double line in Fig. 7.1 (reasoning-based tasks). Compared to the more balanced task (the dashed line), the curve for the reasoning-based tasks reflects the idea that effort will result in performance increases, but at a relatively slow rate. Moreover, for reasoning tasks, older workers may not perceive a payoff in terms of increased performance at high levels of effort. For instance, learning

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a new software program or upgrading to a completely new computer system may be perceived as requiring intense attentional effort for workers who are not knowledgeable about technology. Workers may also perceive that the effort will not pay off in terms of increased performance if the program seems extraordinarily complex. If a worker perceives that the effort required to achieve proficiency on a task is too great—for example, if the effortperformance function is perceived to be flat—workers may choose not to engage in the task at all. The effort-performance relationship will vary depending on perceptions of task complexity in the context of a person’s cognitive abilities (Beier & Oswald, 2012; Kanfer & Ackerman, 2004; Norman & Bobrow, 1975). Changes in abilities through the lifespan will also be reflected in people’s beliefs about their own abilities (Beier, 2008). For instance, difficulty remembering information may be attributed to age-related losses, which may reduce self-efficacy for learning new tasks and increase perceptions of the amount of effort required to learn new skills. Conversely, when tasks are more dependent on knowledge a worker already possesses, task performance will be perceived as relatively less effortful, and a worker may thus be more likely to engage. Two important caveats are necessary in describing the effort-performance functions in Fig. 7.1. First, it would be incorrect to conclude that older workers will be unqualified and unmotivated for jobs that require focused attention such as learning and development. Rather, as described above, learning will only be perceived as resource insensitive when workers know very little about the domain in question. Luckily, as people gain experience with age, they no longer have to reason through every new situation without any baseline knowledge; rather, they bring a vast repertoire of knowledge from prior experiences and education to bear on each new situation. This knowledge may be task specific (e.g., how to create a budget or Gantt chart) or more generic (e.g., how effectively to ask for help, write a professional email, or run a meeting), but all of this knowledge will reduce perceptions of effort required for engaging in job tasks. Even for tasks that are completely new, there are likely elements of past experiences from which older adults can generalize to assist their performance (Beier et al., 2018). The second caveat is related to the tasks themselves in the context of an entire job. The myriad ways mature workers can benefit from their knowledge and expertise in the context of an entire job should serve to boost perceptions of effort and self-efficacy for completing an array of job-related tasks, even if those tasks are completely novel and/or tap reasoning abilities. It is, for example, difficult to think of a job that relies exclusively on reasoning through novel problems—for which existing knowledge of how to manage oneself, a project, or a relationship—would not be relevant. An examination of the importance of different abilities in O NET suggests that the job for which reasoning type abilities are most important is air traffic

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controller (Beier, Torres, & Beal, in press). Even in this job, however, effective air traffic controllers need to know about rules of engagement (e.g., separation conflicts), the physical dimensions of their sectors, procedures for takeoff and landing, and many other things related to landing planes, communicating with pilots, and getting along with others in the workplace. The point is that complex jobs tend to include tasks that tap both reasoning and knowledge abilities, but the relative importance of these abilities may change in ways that influence both the resources one can apply to task completion and the motivation one has to engage in his or her job and/or work-related learning and development. Performance-Utility Function. Kanfer and Ackerman (2004) proposed that the utility of engaging in certain tasks will change when the value of the outcomes associated with them changes with age. For example, younger workers will be likely to perceive utility in increasing levels of performance because they value the associated outcomes, such as increases in pay, promotion, and status in an organization. For older workers, occupational attainment is likely to play a smaller role in their lives given shifts in goals from achievement to socio-emotional through the lifespan (Carstensen et al., 1999). Furthermore, older workers who perceive that they are at the top of their profession and want to increase their connections to community (family and friends) will not perceive increases in job performance to be as useful for their goals. As such, the utility of activities such as work-related skill development, networking, and other activities that are instrumental for attaining achievement-related goals will theoretically be affected by shifting goals with age. Kanfer and Ackerman’s (2004) theory focused mainly on task performance and did not consider contextual performance or organizational citizenship behavior. Here we expand Kanfer and Ackerman’s (2004) consideration of the performance-utility function to include contextual performance. A shift in goals from task to socioemotional should affect the utility of both types of performance. Although contextual performance may include myriad tasks that vary on socio-emotional content, older workers should value the interpersonal aspects of performance that add to the social context of the organization such as helping others complete complex task assignments and mentoring junior colleagues. Indeed, meta-analytic research suggests that although there is no significant relationship between worker age and job performance overall, there is a positive relationship between age and contextual performance (Ng & Feldman, 2008). The performance-utility function for mature workers is shown in Fig. 7.2. The dashed line represents hypothesized task performance, and the solid line represents hypothesized contextual performance. The figure shows that the utility of both task and contextual performance will likely asymptote at relatively lower levels of performance given that the value of performance outcomes such as pay, promotion, and work-derived self-esteem may diminish

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FIGURE 7.2 Performance-utility function for mature workers. Solid line represents contextual performance; dashed line represents task performance. Figure adapted from Kanfer and Ackerman (2004), showing both task and contextual performance.

with age. The figure also shows that the average utility of contextual performance will be higher than the average utility of task performance for older workers, given the shift in goals from achievement to socioemotional with age (Carstensen et al., 1999). This is because socioemotional goals are more likely to be associated with behavior related to contextual performance (e.g., mentoring others, developing interpersonal relationships to increase the positive affect among coworkers) than related to task performance. Effort-Utility Function. The shift in goal type is also expected to influence the effort expended in a task through its influence on utility. Kanfer and Ackerman (2004) argue that the shift in goals from achievement to socioemotional with age will affect the utility of performance, and thus the utility of expending extensive effort to perform a task will be limited for mature workers, particularly when expending effort on a task would be considered stressful or related to depletion of resources (physical, emotional, or mental). As people’s lives become more complex with age, particularly as related to non-work demands such as aging parents and children (Guberman, Lavoie, Blein, & Olazabal, 2012), older workers may adopt an effort conservation approach to work (Baltes, 1987; Kanfer & Ackerman, 2004). A declining effort-utility function is perhaps most reflective of matureworker motivation in the domain of task versus contextual performance. A hypothesized effort-utility function is shown in Fig. 7.3. The dashed line shows that the utility of task performance will be high at low levels of effort (when engaging in the task is not taxing), but that utility for task performance will decline as effort increases. The solid line shows the hypothetical effort-utility function for contextual performance, which remains relatively high and decreases at only the highest levels of performance. For example, the utility of attending a training session to obtain a pay increase or

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FIGURE 7.3 Effort-utility function for contextual (solid line) and task (dashed line) performance. Figure adapted from Kanfer and Ackerman (2004), showing both task and contextual performance for mature workers.

promotion should be diminished for older workers relative to the utility of expending effort to mentor a junior colleague, which may be preserved.

IMPLICATIONS AND FUTURE RESEARCH Taken together, the effort-performance, performance-utility, and effort-utility functions described above have implications for task and contextual performance and for motivation for training and development. Perceptions of the utility of work may decrease as people age, and this decrease in utility will be coupled with increases in perceptions of the amount of effort required to achieve high levels of work performance and increases in the estimates of the effort involved in investing time in training and work-related development activities. The overall result will be a trend toward decreased motivation for work performance and developmental activities as workers age, particularly when reasoning versus knowledge abilities are tapped for performance. It is important to note, however, that the decline in the utility of work with age is not universal. The truth is that there is immense variability in aging and that the utility of work will remain high for many workers who perceive work as satisfying, for whom work is central to identity (Beier, LoPilato, & Kanfer, 2018; Paullay, Alliger, & Stone-Romero, 1994), and for workers who continue to seek development opportunities offered through the workplace. For these workers, work will continue to offer experiences that are useful for meeting their overarching goals (Kanfer, Beier, & Ackerman, 2013). More practically, judgments about the utility of work will be affected

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by financial concerns related to retirement and to daily survival. That is, for many people—particularly in countries that do not have federally supported retirement—working past normal retirement age will have less to do with achievement and socio-emotional goals, but will be more related to the practicalities of earning enough money to remain comfortable during retirement (Quinn & Cahill, 2016). The functions described above highlight the mechanisms that affect worker engagement in performance and development activity, and, as such, they point to possible levers for workplace interventions. Unfortunately, there is very little published research that examines the effectiveness of workplace interventions for workers across the working lifespan (Truxillo, Cadiz, & Hammer, 2015). The research that has been conducted suggests that mature workers can remain engaged and productive if they perceive opportunities as relevant to them and if they perceive that they can effectively engage in those opportunities. For instance, a series of interviews conducted by Zwick (2011), suggested that older workers were not disinterested in work-related development. Rather, older workers desired training and development opportunities that were directly relevant to their daily activities or relevant to a problem they were trying to solve in the moment (as opposed to more general training and development). Moreover, older workers were less interested in training that focused on content with which they were unfamiliar (e.g., technical training) than they were on enhancing existing skills (e.g., management). Linking these interviews to lifespan theories suggests that organizations can continue to engage workers by providing training and development activities that are relevant to the goals of older workers (affecting perceptions of the utility of performance) and training opportunities that are aligned with the knowledge older workers have accumulated through their vast work experience (affecting perceptions of the effort involved in task engagement). Moreover, these findings suggest that training content will be more appealing to older workers when it is aligned with their socioemotional goals such as developing relationships and mentoring others (Carstensen et al., 1999). Additional quantitative research further debunks the notion of a universal decline in abilities and motivation with age and suggests that jobs can be designed to promote continuous engagement through the working lifespan. Autonomy as related to scheduling one’s workday, for instance, has been found to have significant positive effects on task performance, adaptive performance, and proactive performance for older workers (Goˇstautait˙e & Buˇci¯unien˙e, 2015). Schedule autonomy may permit workers to balance work and life constraints and may allow workers to engage in work at times when they feel most effective, affecting perceptions of effort involved in working. Social support was also related to worker proactive performance in the Goˇstautait˙e and Buˇci¯unien˙e study, further highlighting the importance of interpersonal connections as workers age and pointing to possible

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interventions to highlight the socioemotional aspects of work that might affect the utility of effort and performance for older workers. Indeed, Stamov-Roßnagel and Biemann (2012) found that age was positively associated with motivation for generativity-related but not growth-related tasks. Future research. Above we highlighted the idea that the utility of work for older workers may be affected by the type of performance (task or contextual) given shifts in worker goals through the lifespan. Although research suggests that older workers tend to be rated higher in contextual performance relative to younger workers (Ng & Feldman, 2008), and that older workers are more motivated to engage in generative workplace behavior such as mentoring others (Kooij, de Lange, Jansen, Kanfer, & Dikkers, 2011), very little theory or research has specifically addressed contextual performance in the context of workplace aging. Future work in this area could explicitly examine the performance-utility and effort-utility functions for contextual performance described in Figures 7.2 and 7.3. For instance, researchers could examine the extent to which older workers rate the utility of contextual performance higher than task performance (Fig. 7.2) and whether there is a difference in the effort older workers are likely to expend on contextual-related versus task-related tasks (Fig. 7.3). Our first research recommendation thus is related to an increased focus on the relationship between age and contextual performance in organizational science. The effort-performance functions described above and by Kanfer and Ackerman (2004) suggest that judgments of effort will only be significantly negatively affected when job tasks tap relatively more reasoning than knowledge abilities. Organizational scientists, however, know very little about the ability demands of specific job tasks. The continued reliance on general mental ability in research and practice in organizational science is understandable, given the power of this construct to predict overall job performance (Schmidt & Hunter, 1998). But, because most measures of general mental ability rely heavily on abstract problem solving and general cultural knowledge, they are more reflective of reasoning versus knowledge abilities and as such do not give older workers credit for what they know (Schneider & Newman, 2015). Moreover, the over-reliance on general mental ability in organizational science has left us with a literature that provides scant information about how different types of abilities like reasoning and knowledge are related to both task and contextual job performance. O NET (National Research Council, 2009) can be helpful in delineating the importance of specific abilities for job performance for the 1,000 or so jobs included therein, but the organization and level of specificity of O NET does not align with the reasoning and knowledge ability constructs examined in cognitive psychology. Understanding job tasks that tap reasoning versus knowledge abilities could help organizations design jobs that mitigate perceptions of effort and potential performance decrements that results from the effortperformance function shown in Fig. 7.1. Our second research

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recommendation is to explore task demands in terms of reasoning and knowledge abilities within occupations to inform job design for a future of work that will include a larger percentage of older workers than ever before. An additional area of future research is to investigate the feasibility of developing assessments that can be used in the selection context to measure the knowledge and expertise possessed by older workers. Beier et al. (2018) describe a taxonomy of job knowledge that crosses procedural knowledge (knowing how) and declarative knowledge (knowing what) with task and contextual performance. This taxonomy can be applied to any job to permit organizational scientists to determine the type of knowledge that is relevant for job performance so that new measures can be developed for use in selection and assessment. An analysis of job-relevant knowledge can further highlight whether this knowledge is general across jobs (e.g., how to develop a budget or manage a project) versus specific to a job (e.g., knowledge about how the strategic mission of the organization applies to a worker’s daily routine). Identifying relevant knowledge and the generality versus specificity of this knowledge will be important in developing relevant knowledge assessments. Moreover, research suggests that knowledge-based assessments are less likely to show age-related adverse impact than reasoning-ability based assessments. We can imagine, for instance, selection tools to assess general job-related knowledge (e.g., project management, budgeting, and people management) will become increasingly important as the workforce—and applicant pool—ages (Beier et al., 2018). Indeed, practitioners have called for an increased focus on developing relevant job knowledge assessments (Ryan & Ployhart, 2014). Consequently, our third research recommendation is to invest in the development of general job knowledge measures across task and contextual domains for use in selection and assessment.

CONCLUSION Almost 30 years have passed since Kanfer and Ackerman (1989) highlighted the importance of developing theoretical models for investigating cognitive abilities and motivation in concert. Since that time, this work has been reconsidered in the context of workplace aging; in particular, work motivation across the lifespan (Kanfer & Ackerman, 2004). Here we expanded Kanfer and Ackerman’s seminal work with a consideration of both task and contextual performance across performance and learning outcomes. Clearly there is much work to be done, but organizational scientists are well on their way to understanding the lifespan perspective of the important truism, that performance is a function of ability and motivation (Kanfer, 1990; Mitchell, 1982), acknowledging that both abilities and motivation will change in important and interactive ways throughout one’s life and career.

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