Task Performance Among Employees Above Age 65: The Role of Cognitive Functioning and Job Demand-Control

نویسنده

  • Annet De Lange
چکیده

Owing to the aging of Western societies, an increasing number of people over age 65 are now working in bridge employment. Research is needed to understand how job characteristics in bridge employment should be designed to support older employees’ productivity, considering potential declines in intra-individual resources. Drawing on lifespan development of resources and job design models, we investigated the interplay of cognitive functioning, job demands, and job control, and their impact on task performance, in a sample of workers in bridge employment. In total, 228 employees from a Dutch temporary employment agency that contracts workers aged 65 years and older participated in this longitudinal study, with a 1-year time lag. Of the panel, 74.1% of the respondents were male, and the mean age was 69.02 years (range 65–80 years). Cognitive functioning, job demands, job control, and task performance were assessed two times with thoroughly validated self-report measures. Good cognitive functioning emerged as an essential intra-individual resource in order to maintain good task performance for employees aged 65 years and older. After including the influence of job demands and job control, positive effects of cognitive functioning on task performance remained significant only in a high-strain job (with high job demands and low job control). This outcome suggests that age-related changes in cognitive functioning among employees above the age of 65 years only affect productivity at work when the job demands are too high relative to the available job control. Implications for retirement research and lifespan perspectives of job design research are discussed. Due in part to the aging of the population of Western industrialized countries (e.g., Lutz, Sanderson, & Scherbov, 2008), an increasing number of people above 65 years now work during the period between their retirement decision and complete labor force withdrawal (i.e., they take on bridge employment; e.g., Alcover, Topa, Parry, Fraccaroli, & Depolo, 2014; Giandrea, Cahill, & Quinn, 2009; Gobeski & Beehr, 2009; Kim & Feldman, 2000; Rudolph, De Lange, & Van der Heijden, 2015; Shultz, 2003; Shultz & Wang, 2011; Wang, Zhan, Liu, & Shultz, 2008; Wang & Shi, 2014). In the United States, the majority of older employees start fullor part-time bridge employment before exiting the labor force (Giandrea et al., 2009). Older European employees compared to older U.S. employees, however, tend to move directly into full retirement to a higher extent. Nevertheless, both in Europe and in the United States, there is an overall trend that indicates an increase in bridge jobs (Brunello & Langella, 2012). From the perspective of adjusting to retirement, bridge employment can be regarded as one of the most central factors for aging individuals to cope with the challenges during retirement transition (Rudolph et al., 2015; Shultz & Wang, 2011; Wang, Henkens, & Van Solinge, 2011; Wang & Shi, 2014; Wang & Shultz, 2010): Engaging Task Performance, Cognitive Function, and Job Demand • 297 in bridge employment may promote employee’s physical and mental health (Zhan, Wang, Liu, & Shultz, 2009), retirement satisfaction, and overall life satisfaction (Kim & Feldman, 2000). For working organizations, bridge employment is beneficial because it helps to retain access to the valuable experience that older employees have gained throughout their professional careers, especially during skilled workforce shortages (Alcover et al., 2014; Shultz &Wang, 2011; Wang & Shultz, 2010). From a societal perspective, labor market participation through older age is extremely important because it will relieve the burden on social security and pension systems as older employees continue to contribute. Therefore, further investigation is needed to better understand how we can enhance the added value of employees above age 65 during this stage of the retirement process. Considering the official retirement age (65 years, or slightly above in most European countries), employees are likely to enter bridge employment at an age when decrements of age-sensitive domains of cognitive functioning—such as maintaining information in working memory, or detecting information quickly—might become more relevant (e.g., Schaie, 1994). Moreover, bridge employment frequently involves a change in occupation, industry, or both (e.g., Cahill, Giandrea, & Quinn, 2011). Thus, bridge jobs might not be mastered successfully by drawing mainly on previous job experiences, which typically would help to equalize decrements in cognitive functioning (Baltes, 1997). Bridge jobs might therefore demand a rather considerable level of age-sensitive domains of cognitive functioning because the latter are requirements for learning new routines and skills at work (Kanfer & Ackerman, 2004). In sum, bridge employment can accentuate the discrepancy between age-related losses and challenging contextual job characteristics. Against this background, the present study was guided by the key question: “How should job characteristics in bridge employment be designed in order to guarantee that employees above age 65 are able to remain productive, given potential, relative losses in cognitive functioning?” Our study draws on the theoretical perspective of lifespan development, with an emphasis on gains and losses of resources (Baltes, 1997; Hobfoll & Wells, 1998), and on psychosocial models of job design, such as the Job Demand-Resources Model ( JD-R, Demerouti, Bakker, Nachreiner, & Schaufeli, 2001), and the job demand-control model ( JD-C, Karasek, 1979). From a lifespan development perspective, an aging individual faces dwindling intra-individual resources (e.g., cognitive functioning) that successively outweigh gains of resources (e.g., experience; Baltes, 1997). According to the conservation of resources theory (COR, Hobfoll & Wells, 1998), contextual job characteristics either catalyze or counteract the detrimental impact of these intra-individual resource losses on functioning, personal development, and wellbeing over a lifespan (Westman, Hobfoll, Chen, Davidson, & Laski, 2005). The JD-R model allows for the distinction between those contextual job characteristics that are expected to either amplify or counteract the consequences of intra-individual resource losses. According to the JD-R model, job demands are defined as job characteristics that require physiological and psychological energy from the employee, whereas contextual job resources are defined as job characteristics that support employees in accomplishing job tasks, that directly reduce job demands, or that stimulate resource growth (Demerouti et al., 2001). Consequently, available intra-individual resources of older employees are expected to be more important for favorable intra-individual outcomes when job demands are high and contextual job resources are low (e.g., Rudolph et al., 2015). Building upon these considerations, our approach was specifically inspired by the proposition of the theoretical framework on adult development at the workplace, developed by Kanfer and Ackerman (2004), which proposes that the interplay between age-sensitive domains of cognitive functioning and job demands predicts the performance of older employees at work. According to Borman and Motowidlo (1997; see also Ng & Feldman, 2008), we define performance in terms of good core task performance, that is “the effectiveness with which job incumbents perform activities that contribute to the organization’s technical core,” (p. 99). Transferring the framework by Kanfer and Ackerman to bridge employment, we asked ourselves two questions: “Is cognitive functioning a relevant intra-individual resource for older employees in bridge employment to maintain a sufficient level of task performance?” and “Do higher job demands amplify the expected positive effects of cognitive functioning on task performance in older employees in bridge employment?” To further extend the perspective of our study, we additionally incorporated job control as one of the most important contextual job resources, according to the JD-R model (Demerouti et al., 2001) and the JD-C model (Karasek, 1979). Job control is defined as the extent to which a job allows freedom, independence, and discretion to schedule work, make decisions, and choose the methods used to perform tasks (Morgeson & Humphrey, 2006). Specifically, we investigated the question: “Does job control buffer the expected positive impact of cognitive functioning on task performance in older employees who are exposed to high job demands?” In their lifespan theory of control, Heckhausen and Schulz (1995) postulated that individuals have a basic need to possess control over their environment. Accordingly, job control repeatedly has been shown to facilitate positive work experiences, intrinsic motivation, and enhanced performance outcomes (e.g., De Lange, Taris, Kompier, Houtman, & Bongers, 2003; Humphrey, Nahrgang, & Morgeson, 2007). In particular, in respect to older employees, studies have reported that increased job control helps to counterbalance resource losses (Van den Berg, Robroek, Plat, Koopmanschap, & Burdorf, 2011; Weigl, Müller, Hornung, Zacher, & Angerer, 2013). Our study addresses several gaps in the scholarly literature: Taking the view that bridge employment itself might be an indicator of adjustment to retirement (Shultz & Wang, 2011; Wang et al., 2011; Wang & Shi, 2014; Wang & Shultz, 2010); previous literature on retirement that incorporated job characteristics mainly addressed the question of how pre-retirement jobs affect retirement decisions, such as taking on bridge employment (e.g., Elovainio et al., 2005; Gobeski & Beehr, 2009; Gørtz, 2012; Wang et al., 2008; Wöhrmann, Deller, & Wang, 2013). In a similar vein, the literature on the interplay between cognitive functioning and job characteristics during retirement transition also focused exclusively on the question of whether preretirement job characteristics moderate the change in cognitive functioning both before and after retirement (Finkel, Andel, Gatz, & Pedersen, 2009; Fisher et al., 2014). Little attention has been devoted to the question of how postretirement job design might support older employees, who already are in bridge employment, to remain productive despite potential losses in cognitive functioning (Rudolph et al., 2015). The same is true regarding the lifespan perspective of job design (e.g., Schlick, Frieling, & Wegge, 2013; Truxillo, Cadiz, Rineer, Zaniboni, & Fraccaroli, 2012; Truxillo & Fraccaroli, 2013). Until now, only a few empirical studies 298 • A. Müller et al. have examined how to design jobs for older employees, taking into account normal age-related changes in intra-individual resources, such as cognitive functioning. Moreover, we do not know much about how changes in cognitive functioning above age 65 affect the productivity of employees. Previous research on adult cognitive development has been mainly based on laboratory experiments that test cognitive limits (e.g., Salthouse, 2013), yet applied field research on this issue is still lacking. Therefore, the practical relevance of findings regarding age-related cognitive declines for the work context remains a matter of debate (Kanfer & Ackerman, 2004). We addressed the limitations from previous scholarly work by conducting a longitudinal study among 228 Dutch employees above age 65 who were engaged in bridge employment. Our longitudinal approach, with a focus on the interplay between cognitive functioning, job demands, and job control on task performance, enabled us to gain new insights into the adjustment process during bridge employment. Experiencing good performance has been shown to strengthen personal self-efficacy, and vice versa (Lindsley, Brass, & Thomas, 1995). Along that vein, the positive experience of sustained performance in bridge employment is expected to have a strong personal meaning for older employees in respect to their future positive adjustment to critical transitions during retirement (Rudolph et al., 2015). Additionally, as our study used a sample that is rather homogeneous in terms of chronological age (i.e., the number of years lived) and, likewise, heterogeneous in terms of cognitive functioning, we were able to disentangle the effects of intra-individual age-related changes in cognitive functioning from the effects of chronological age on productivity during later career stages (Ng & Feldman, 2008). Hypotheses Development Cognitive functioning and task performance in bridge employment From a resources perspective (cf. Hobfoll & Wells, 1998), work and organizational psychologists highlighted the impact of cognitive functioning as one of the major intra-individual resources of older employees which allows them to remain productive at work (Kanfer & Ackerman, 2004). Longitudinal studies showed quite consistently that those aspects of cognitive functioning that are summarized under the term fluid intellectual abilities (Cattell, 1987), such as the capability of working memory or perceptual speed, tend to decline over one’s lifespan (e.g., Salthouse, 2013; Schaie, 1994). Notwithstanding the fact that aging is not a uniform process, and inferences from chronological age to cognitive functioning are at risk for bias (e.g., Morse, 1993; Schaie & Hofer, 2001), it is likely that healthy individuals in their midsixties are at an enhanced risk for decline and deterioration in cognitive functioning (e.g., Schaie, 1994). From an occupational point of view, the question arises regarding whether these age-related cognitive decrements might impair task performance in employees aged 65 years and older (Kanfer & Ackerman, 2004). Many jobs do not make demands on the outermost limits of cognitive functioning. Hence, with the exception of highly demanding cognitive jobs, such as air traffic controller (e.g., Müller, Petru, Englmann, & Angerer, 2011), a decrease in, for example, perceptual speed within the range of milliseconds does not pose serious problems for the job performance of older employees (Ng & Feldman, 2008; Warr, 1993). Additionally, older employees might be able to compensate for declines because they accumulated job-specific experience during their career (e.g., Baltes, 1997). For example, Salthouse (1984) investigated the speed and accuracy of keystrokes among typists ranging in age from 19 to 72 years. One interesting finding was that older typists could compensate for a slower reaction time because they were more sensitive in characters farther in advance. Thus, in many jobs, age-related declines of fluid intellectual abilities can be compensated for by the age-related increase of socalled crystallized intellectual abilities, such as experience and expertize (Cattell, 1987). Therefore, in jobs that do not pose exceeding levels of age-critical job demands, healthy and successful aging is usually signified by—and associated with—enhanced task performance (Kanfer & Ackerman, 2004). Bridge jobs, however, may represent a specific case that suggests the need for a closer examination of the impact of cognitive functioning on task performance. As already stated, employees are likely to enter bridge employment at an age in which decrements in cognitive functioning might become more relevant (Schaie, 1994). Additionally, bridge jobs are likely to be novel jobs for the aged jobholders (Cahill et al., 2011). Thus, in contrast to most other jobs, we expect a weaker supporting effect of accumulated job experience that usually buffers age-related declines in older employees (e.g., Baltes, 1997). Moreover, increased cognitive functioning in terms of fluid intellectual abilities boosts quick and successful adaptation through learning and mastery, which is expected to be a strong predictor of performance on the job (Kanfer & Ackerman, 2004). As a result, we assume that good cognitive functioning is a predictor of better task performance in a bridge employment sample. H1: There is a lagged, positive relationship between cognitive functioning and task performance in older employees in bridge employment. The interplay between cognitive functioning , job demand-control, and task performance Consistent with the COR perspective of interacting intra-individual resources and contextual job characteristics (Westman et al., 2005), and building upon previous considerations (Kanfer & Ackerman, 2004), we assume that job demands in bridge employment impact the strength of the association between cognitive functioning and task performance of employees above age 65. As explained above, bridge jobs are often novel jobs that cannot exclusively be accomplished through prior experience and routines that older employees usually have accumulated during their professional careers. Thus, job demands in bridge employment are expected to pose rather high demands on cognitive functioning in terms of the fluid intellectual abilities of older employees, in order to learn necessary routines and skills for performing successfully. Consequently, it is not simply the quantity of job demands in bridge employment that is expected to be relevant, but rather the inherent quality of novelty in job demands (Kanfer & Ackerman, 2004). Thus, we expect that cognitive functioning has a stronger positive impact on task performance for older employees with high job demands in bridge employment compared to older employees in bridge employment with low job demands. H2: Job demands moderate the lagged positive association between cognitive functioning and task performance in Task Performance, Cognitive Function, and Job Demand • 299 older employees in bridge employment, such that there is a stronger association under high job demands compared to low job demands. Consistent with the JD-C model (Karasek, 1979), we further assume that the interaction between job demands and job control affects the strength of the lagged relationship between cognitive functioning and task performance. The JD-C model proposes four possible combinations of high/low job demands and high/low job control: (a) highstrain jobs, (b) active jobs, (c) passive jobs, and (d) low-strain jobs. These four types are important combinations of job characteristics because they signify entirely different regulatory demands on the employee (Karasek, 1979). High-strain jobs (combining high job demands and low job control) are jobs with a high risk for stress and poor health experiences (e.g., De Lange et al., 2003). Consistent with the resources perspective (Hobfoll & Wells, 1998), we assume that high-strain jobs, on the one hand, put high demands on the older employee, but, on the other hand, provide only limited possibilities to apply effective individual strategies that would allow for the compensation of intra-individual agerelated declines (Van den Berg et al., 2011). This perspective is in line with very recent theoretical considerations regarding the adjustment to bridge employment, which have suggested that intra-individual resources are more important when there is an imbalance in terms of low contextual job resources and high job demands, and, correspondingly, when people are less likely to rely on intra-individual resources as contextual job resources meet job demands (Rudolph et al., 2015). We therefore assume that in high-strain jobs, cognitive functioning is most crucial for task performance compared to other kinds of job demand-control types, especially because such high-strain jobs impose on the employee the most unfavorable combination of high regulatory demands and low job control possibilities (Hacker, 2003). Like high-strain jobs, active jobs are also characterized by high job demands, but at the same time, provide high job control. Again, from a resources and adjustment perspective, we can expect that job control in active jobs contributes to the maintenance and growth of professional capacity in older employees. Maintenance should be enhanced because, contrary to high-strain jobs, active jobs allow older employees to apply behavioral strategies to compensate for diminished cognitive functioning and to effectively cope with high job demands (Weigl et al., 2013). One of the few available studies that put the JD-C model into the lifespan perspectives of job design was conducted by Shultz, Wang, Crimmins, and Fisher (2010). Their findings, using a representative European sample of working adults, indicated that older employees appear to be particularly in need of job control to reduce the experience of stress from job demands. The authors interpret this outcome considering potential cognitive declines, such that job control might allow older employees, for instance, to schedule cognitively demanding tasks during times when distractions are minimized, in order to compensate for declines in cognitive inhibition. Moreover, active jobs should support growth because they are expected to lead to an increase in learning and problem-solving activities (De Lange et al., 2010; De Witte, Verhofstadt, & Omey, 2007; Taris & Kompier, 2004), and therefore allow for the acquirement of new routines to better meet the respective demands of bridge jobs in the future. Thus, in respect to our research question, we assume that in active jobs, cognitive functioning of employees above age 65 in bridge employment is positively related to task performance because active jobs pose high demands. However, we assume that the association between cognitive functioning and task performance in active jobs is weaker than in high-strain jobs. This is because the job control available in active jobs should allow for the compensation of age-related declines and adoption of new behavioral routines. In contrast to job demand-control combinations that impose high job demands, we do not assume that job control affects the relationship between cognitive functioning and task performance in jobs with low demands; that is, passive jobs (low job demands and low job control) and low-strain jobs (low job demands and high job control). According to our previously described rationale, the degree of cognitive functioning is expected to be less important for task performance when job demands are low. Along the same vein, the similar supporting effects of job control are expected to be weaker. Thus, for older employees in passive and low-strain jobs, there are only minor needs to compensate for age-related declines or to learn new routines. To sum up our last hypotheses, we assume that the combination of job demands and job control moderates the lagged relationship between cognitive functioning and task performance of employees above age 65 in bridge employment. Additionally, we expect that job control affects the relationship between cognitive functioning and task performance in jobs that encompass high job demands, such that effects of cognitive functioning are strongest in jobs that contain high job demands and low job control (high-strain jobs) compared to other job demand-control types. H3: The combination of job demands and job control moderates the lagged positive association between cognitive functioning and task performance in older employees in bridge employment. H3a: The lagged positive association between cognitive functioning and task performance in older employees in bridge employment is stronger in high-strain jobs compared to other job demand-control types (active, passive, and low-strain jobs). H3b: The lagged positive association between cognitive functioning and task performance in older employees in bridge employment is stronger in active jobs compared to passive and low-strain jobs. METHOD Sample Data for the present study were part of a larger two-wave panel study conducted in 2011 and 2012 in the Netherlands (as part of the European Union). Wave one of this study took place in May, 2011. Initially, all registered clients of a temporary employment agency that specifically contracts workers older than age 65 were invited to participate (N = 6,538; 74.80% males, Mage = 69.70 years). Of the invited workers, n = 784 active workers responded to an online questionnaire (response rate = 11.99%). In May, 2012, a follow-up wave of data was collected. Again, all registered clients of the same employment agency were invited to participate. Of the invited workers, n = 655 completed the online questionnaire at Time 2 (response rate = 10.01%). Considering both time points (T1 and T2), n = 228 respondents 300 • A. Müller et al. completed both waves, and these individuals constitute the panel considered here. In all, 74.1% of this panel’s respondents were male, with a mean age of 69.02 years (SD = 3.08 years; range 65–80 years). The largest part had a bridge employment position in the education and science sector (32%), followed by transportation and delivery (19.7%), and office work (18%). Of our sample, 14.2% reported that they currently did not work actively at the time of the first or the second wave. On average, the respondents had worked 5.33 years (SD = 9.30) in their current positions. The median tenure was three years (i.e., 61% of the employees worked three years or less in their current role). Thus, for the majority of employees in this sample, the bridge job was a new job compared to their previous employment, which is in line with the typical feature of bridge jobs.

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تاریخ انتشار 2017