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Occupational future time perspective (OFTP) refers to employees’ perceptions of their future in the employment context. Based on lifespan and organizational psychology theories, we review research on OFTP and offer a meta-analysis of antecedents and outcomes of OFTP (K = 40 independent samples, N = 19,112 workers). Results show that OFTP is associated with individual characteristics and personal resources, including age (ρ = -0.55), job tenure (ρ = -0.23), organizational tenure (ρ = -0.25), educational level (ρ = 0.16), and self-rated physical health (ρ = 0.16), as well as job characteristics, like job autonomy (ρ = 0.22). Moreover, OFTP is related to important work outcomes, including job satisfaction (ρ = 0.28), organizational commitment (ρ = 0.41), work engagement (ρ = 0.22), retirement intentions (ρ = -0.37), and work continuance intentions (ρ = 0.16). OFTP is also related to task (ρ = 0.11) and contextual performance (ρ = 0.20). Additional analyses show that OFTP predicts job attitudes and work performance above and beyond the effects of another developmental regulation construct, selection, optimization, and compensation (SOC) strategies. Overall, the findings of our meta-analysis suggest that OFTP is an important construct in the context of an aging workforce.
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Occupational Future Time Perspective: A Meta-Analysis of Antecedents and Outcomes
Cort W. Rudolph
Saint Louis University
Dorien T. A. M. Kooij
Tilburg University
Rachel S. Rauvola
Saint Louis University
Hannes Zacher
Leipzig University
Author Note
Cort W. Rudolph, Department of Psychology, Saint Louis University, Saint Louis, USA.
Dorien T. A. M. Kooij, Department of Human Resource Studies, Tilburg University, the
Netherlands. Rachel S. Rauvola, Department of Psychology, Saint Louis University, Saint Louis,
USA. Hannes Zacher, Institute of Psychology, Leipzig University, Germany.
Portions of this work were presented at the 2017 Aging and Work Meeting, Lüneburg,
Germany, EU.
Correspondence concerning this article should be addressed to Cort W. Rudolph, Saint
Louis University, Morrissey Hall 2827, St. Louis, MO, 63103, cort.rudolph@health.slu.edu,
+1(314) 977-7299
Please Cite As:
Rudolph, C.W., Kooij, D.T.A.M., Rauvola, R.S., & Zacher, H. (2018). Occupational future time
perspective: A meta-analysis of antecedents and outcomes. Journal of Organizational
Behavior [In Press Accepted Manuscript].
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Abstract
Occupational future time perspective (OFTP) refers to employees’ perceptions of their future in
the employment context. Based on lifespan and organizational psychology theories, we review
research on OFTP and offer a meta-analysis of antecedents and outcomes of OFTP (K = 40
independent samples, N = 19,112 workers). Results show that OFTP is associated with
individual characteristics and personal resources, including age (ρ = -0.55), job tenure (ρ = -
0.23), organizational tenure (ρ = -0.25), educational level (ρ = 0.16), and self-rated physical
health (ρ = 0.16), as well as job characteristics, like job autonomy (ρ = 0.22). Moreover, OFTP is
related to important work outcomes, including job satisfaction (ρ = 0.28), organizational
commitment (ρ = 0.41), work engagement (ρ = 0.22), retirement intentions (ρ = -0.37), and work
continuance intentions (ρ = 0.16). OFTP is also related to task (ρ = 0.11) and contextual
performance (ρ = 0.20). Additional analyses show that OFTP predicts job attitudes and work
performance above and beyond the effects of another developmental regulation construct,
selection, optimization, and compensation (SOC) strategies. Overall, the findings of our meta-
analysis suggest that OFTP is an important construct in the context of an aging workforce.
Keywords: aging, focus on opportunities, meta-analysis, future time perspective, remaining time
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Occupational Future Time Perspective: A Meta-Analysis of Antecedents and Outcomes
Due to demographic, economic, and societal changes, many employees expect, want, or
have to work longer—sometimes even well beyond the traditional retirement age (Bal, Kooij, &
Rousseau, 2015; Truxillo, Cadiz, & Hammer, 2015). Additionally, individuals are increasingly
expected to take long-term responsibility for managing their own careers (Gubler, Arnold, &
Coombs, 2014). Research suggests that proactivity and adaptability are important for career
success (e.g., Rudolph, Lavigne, & Zacher, 2017; Rudolph, Lavigne, Katz, & Zacher, 2017;
Tornau & Frese, 2013). Proactive and adaptive behaviors require that employees adopt a long-
term perspective to anticipate and plan for their occupational future (Savickas, 1997; Strauss,
Griffin, & Parker, 2012). One concept that captures this focus toward the future is occupational
future time perspective (OFTP). Based upon research in the lifespan developmental literature
(Carstensen, Isaacowitz, & Charles, 1999; Cate & John, 2007), Zacher and Frese (2009) defined
OFTP as individuals’ perceptions of their future in the employment context. They distinguished
between two dimensions of OFTP (i.e., perceived remaining time and focus on opportunities)
and showed that both were negatively related to employee age. The negative association between
OFTP and age was replicated in several subsequent studies (e.g., Froehlich, Beausaert, & Segers,
2016). Moreover, empirical studies conducted over the past decade have demonstrated positive
associations between OFTP and important work outcomes, including job satisfaction, work
engagement, and work performance (Schmitt, Zacher, & de Lange, 2013; Weikamp & Göritz,
2016; Zacher, Heusner, Schmitz, Zwierzanska, & Frese, 2010).
Although a recent qualitative review of studies on OFTP points to the general importance
of OFTP in the work context (Henry, Zacher, & Desmette, 2017), a quantitative synthesis and
integration of research on antecedents and outcomes of OFTP is currently lacking. To address
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this gap, we present results of a meta-analysis of the OFTP literature to guide future research and
organizational practice. Compared to Henry and colleagues’ (2017) qualitative literature review,
our quantitative meta-analysis has at least three notable differences. First, our meta-analysis
quantitatively combines findings from multiple studies into precise estimates of the true
population relationships between OFTP and commonly investigated antecedents and outcomes.
Researchers have argued that meta-analyses yield more accurate and credible conclusions than
qualitative reviews, which may be consciously or unconsciously biased (Rosenthal & DiMattheo,
2001). Second, while Henry and colleagues (2017) discussed only published research in their
qualitative review, we include both published and unpublished data in our meta-analysis to
address the “file drawer problem” (i.e., a bias in the published literature toward statistically
significant effects; Rosenthal, 1979). Finally, using meta-analytic regression and path analyses,
we offer evidence to differentiate OFTP from both chronological age and selection, optimization,
and compensation (SOC) strategies as a predictor of important work outcomes. SOC strategy use
is another prominent construct from the lifespan developmental literature that is increasingly
investigated in the work context (Moghimi, Zacher, Scheibe, & Van Yperen, 2017). SOC
strategies constitute proactive behaviors that involve the selection of one’s most important goals,
optimization of goal pursuit, and compensation for the loss of goal-relevant means (Baltes &
Baltes, 1990; Freund & Baltes, 2000; 2002). Given its conceptual and empirical links with
chronological age and SOC strategy use (Zacher & Frese, 2011), distinguishing OFTP as a
unique predictor is important for establishing its distinctiveness from other constructs within the
lifespan development nomological network.
We aim to contribute to the organizational behavior literature in several meaningful
ways. First, we quantitatively summarize relationships between OFTP and various antecedents
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and outcomes. With longer working lives becoming the norm, OFTP represents an important
temporal construct for understanding the complexities of the age variable within contemporary
work contexts. Second, to build support for our meta-analytic investigation, we outline the
development of OFTP and its dimensions across various studies. This discussion serves to
bookend a review of research concerning relationships between OFTP and a variety of personal
and work-related constructs. To organize our review, we offer an integrative model of the
existing nomological network of OFTP and associated constructs. This model summarizes
general relationships between individual and job characteristics, as well as various work
outcomes that have been studied along with OFTP. Moreover, we examine the unique predictive
validity of OFTP beyond chronological age and SOC strategy use (Baltes, Wynne, Sirabian,
Krenn, & de Lange, 2014; Zacher & Frese, 2009), both of which have also been linked to
important work outcomes (see Brewer & Shapard, 2004; Moghimi et al., 2017; Ng & Feldman,
2008, 2010). Finally, since age is associated with both OFTP and SOC, and because recent
theoretical developments concerning successful aging at work have called for the testing of
process models that include age-related mediators (Zacher, 2015), we also examine the indirect
effects of age on work outcomes through these competing developmental mechanisms. This
analysis also responds to a recent call by Rudolph (2016) to conduct integrative tests of the
various developmental regulation mechanisms proposed by different lifespan developmental
theories. Our process model addresses this call by exploring how OFTP and SOC as two
developmental mechanisms operate in tandem with one another and link age to work outcomes.
More practically speaking, our findings contribute to the organizational behavior and
human resources management knowledge base. The results of our meta-analysis provide OB/HR
professionals with theoretically grounded and empirically supported ideas on how to enhance
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employees’ OFTP through job redesign efforts that indirectly influence important work
outcomes. With these goals in mind, next we elaborate on the theoretical models that ground this
work and then outline the methods and results of our meta-analysis. We conclude by discussing
limitations and implications of the present work, along with recommendations for future research
based upon our findings.
Occupational Future Time Perspective
The OFTP construct originates from research in the lifespan developmental literature on
the general or context-free notion of future time perspective (FTP). FTP is a core construct in
socioemotional selectivity theory (Carstensen, 1991, 2006; Carstensen et al., 1999), which
suggests that FTP decreases with age and predicts changes in the priority of individuals’ social
goals. Specifically, younger people, who tend to have an expansive FTP, prioritize instrumental
and knowledge-related goals (e.g., meeting a broad variety of new people) that help them
maximize gains in the future. In contrast, older people typically have a more constrained FTP
and are therefore thought to prioritize meaningful and positive goals in the present (e.g., meeting
close social partners, mentoring). In the lifespan developmental literature, general FTP is
typically assessed with a 10-item self-report scale developed by Carstensen and Lang (1996;
Lang & Carstensen, 2002). FTP differs from other temporal constructs such as time orientation
(Zimbardo & Boyd, 1999) and temporal focus (Shipp, Edwards, & Lambert, 2009), which refer
to individual difference characteristics that are relatively stable across the lifespan.
Zacher and Frese (2009) adapted the FTP concept to the employment context; OFTP
concerns people’s perceptions of their occupational future time. They conceptually distinguished
two related dimensions of OFTP and assessed them with an adapted version of Carstensen and
Lang’s (1996) FTP scale. Perceived remaining time describes individuals’ perceptions of the
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amount of future time they expect to spend in employment. Zacher and Frese (2009) showed that
perceived remaining time was strongly negatively associated with age, suggesting that older
employees perceive their remaining time at work as more limited than younger employees. The
second dimension of OFTP, focus on opportunities, captures individuals’ perceptions of new
work-related goals, possibilities, and opportunities that are foreseen in the future. Zacher and
Frese (2009) showed that focus on opportunities was moderately negatively related to age, and
that high levels in two motivational job characteristics (job autonomy and complexity) buffered
this relationship.
Most studies have operationalized either only one of the two OFTP dimensions (e.g.,
perceived remaining time, Kooij & Zacher, 2016; e.g., focus on opportunities, Zacher et al.,
2010) or have combined all 10 items into an overall OFTP score (e.g., Ho & Yeung, 2016; see
Henry et al., 2017, for a review). The combination of these two dimensions into an overall OFTP
score can be justified by a relatively strong positive relationship noted in primary studies (e.g., r
= 0.60 reported by Zacher & Frese, 2009). Despite this, evidence suggests that perceived
remaining time and focus on opportunities are conceptually and empirically distinct from one
another (i.e., as shown by factor analysis; Zacher & Frese, 2009). Subsequent studies adopting a
psychometric focus (e.g., Kochoian, Raemdonck, Frenay, & Zacher, 2017; Weikamp & Göritz,
2016) have replicated this two-factor structure of OFTP proposed by Zacher and Frese (2009).
Beyond their factorial validity and distinctiveness, previous conceptual and empirical work
suggests that the two dominant dimensions of OFTP are positively related to one another (e.g.,
Froehlich et al., 2016; Weikamp & Göritz, 2015). To explore the extent of this relationship, we
meta-analytically estimate the strength of the association between perceived remaining time and
focus on opportunities.
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Additionally, in a study with unemployed job seekers, Zacher (2013) factor analyzed the
10 OFTP items and identified three distinct dimensions: perceived remaining time, focus on
opportunities, and focus on limitations. This three-factor structure is consistent with research in
the lifespan developmental literature (Cate & John, 2007; Rohr, John, Fung, & Lang, 2017).
Specifically, Cate and John (2007) argued that focus on opportunities and focus on limitations
are not endpoints on the same underlying dimension, but that individuals can perceive limitations
in their remaining lifetime, while also perceiving some remaining opportunities in the future.
However, with the exception of Zacher (2013), all other studies concerning OFTP have focused
either on perceived remaining time, focus on opportunities, or overall OFTP.
Zacher and Frese (2011) argued that OFTP is distinct from other individual difference
constructs such as optimism and self-efficacy, and that the maintenance of high levels of OFTP
among older workers can be used as an indicator of successful aging at work. Moreover, Zacher
and colleagues suggested that OFTP serves as a developmental regulation mechanism in that
high OFTP leads to improved occupational wellbeing, job attitudes, and performance (Schmitt,
Gielnik, Zacher, & Klemann, 2013; Schmitt, Zacher, & de Lange, 2013). They also demonstrated
that employee age has indirect effects on these favorable work outcomes via OFTP (Gielnik,
Zacher, & Frese, 2012; Zacher et al., 2010). Thus, OFTP appears to have a motivational and
salutogenic function in the work context. High levels of OFTP seem to be particularly important
among older workers because, on average, OFTP declines with age, and variance in OFTP
increases with age (Zacher & Frese, 2011).
Antecedents of Occupational Future Time Perspective
Figure 1 shows our integrative model of the existing nomological network of OFTP and
its associated constructs. This model serves to provide an overview of those antecedents and
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outcomes of OFTP that have been most commonly studied in the literature, and that we have
included in our meta-analysis. To serve as a visual summary of this literature, Figure 1 also
depicts the patterns of relationships between OFTP and these variables in terms of the
directionality most generally assumed and/or observed. Importantly, Figure 1 serves only as a
conceptual representation insomuch as the separation of variables into antecedents and outcomes
is not intended to imply that empirical studies have necessarily identified causal relationships.
Rather, this representation serves as a conceptual summary of the literature on OFTP to organize
our meta-analytic review. With this understanding, we next expand upon the linkages
represented within this model.
Individual characteristics and personal resources. The first set of antecedents
considered in our meta-analysis consists of individual characteristics and personal resources,
including age, gender, job and organizational tenure, educational level, and self-rated physical
health (see Figure 1). Age, job tenure, and organizational tenure are temporal variables, and, as
such, they have been commonly studied in relation to OFTP (e.g., Barbieri, Zurru, Cossu, &
Farnese, 2015; Ho & Yeung, 2016). Research concerning links between such temporal variables
and OFTP typically invokes explanations borrowed from Carstensen’s socioemotional selectivity
theory (Carstensen, 1991, 2006; Carstensen et al., 1999). From this perspective, older employees
and employees with high job and organizational tenure tend to have less time left in their job and
with their organization due to mandatory, forced, or voluntary retirement (e.g., Zacher & Frese,
2009). In addition, many organizations specifically invest in younger workers that have just
entered the organization or in middle-aged employees that are progressing in their careers (e.g.,
Maurer, Weiss, & Barbeite, 2003). Older workers themselves also tend to be less invested in
their career development than younger workers (Colquitt, LePine, & Noe, 2000; Maurer et al.,
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2003). These observations are echoed in research that points more directly toward negative
associations between temporal variables and domain-general FTP. For example, Cate and John
(2007) report that relatively younger adults report higher focus on opportunities than relatively
older adults. The same conclusion was reached by Zacher and de Lange (2011). Thus, it is
perhaps not surprising that research generally reports that perceived remaining time, focus on
opportunities, and overall OFTP are lower among older workers, and those with longer job and
organizational tenure, compared to younger workers and those with shorter job and
organizational tenure.
Beyond these time-related demographic characteristics, past research has commonly
considered gender and educational level as demographic characteristics. Consistent with the
gender similarities hypothesis (Hyde, 2005), research has demonstrated equivocal relationships
between gender and OFTP (e.g., Zacher & Frese, 2009, 2011). However, clearer arguments for
relationships between education level and OFTP exist. For example, past research has justified
generally positive relationships between education level and OFTP (e.g., Schmitt, Zacher, & de
Lange, 2013; Weikamp & Göritz, 2016). This suggests that those with more advanced education
tend to have higher intentions to work beyond traditional retirement age (Griffin & Hesketh,
2008). In addition, highly educated employees show higher performance on the job (Ng &
Feldman, 2009). Since organizations are likely to provide their highly educated and high-
performing employees with more work-related opportunities (Rosen, 1981), it has been argued
that employees with higher educational levels are likely to perceive more occupational
opportunities and a longer occupational future.
General self-rated health (i.e., subjective physical health status) has also been studied as
an antecedent of OFTP, and thus we examine such relationships in our meta-analysis. Zacher et
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al. (2010) argued that personal resources, such as health, may contribute to perceptions of future
time and opportunities, because they may help individuals work better and for longer. Similarly,
Cate and John (2007) proposed that declines in health and energy may result in a decline in focus
on opportunities. This reasoning is in line with conservation of resources theory (Hobfoll, 1989),
which proposes that people strive to obtain, retain, and protect personal resources, such as
perceived remaining time and focus on opportunities, by utilizing other resources, such as health.
This process is known as a “gain cycle” (Hobfoll & Wells, 1998). Following this line of
reasoning, research has argued that people with better self-rated health invest these resources to
gain additional resources, including higher OFTP. Indeed, Zacher and Frese (2009) found that
both perceived remaining time and focus on opportunities were positively related to subjective
physical health, and Kooij and van de Voorde (2011) found that subjective general health
positively predicted focus on opportunities. These findings and the arguments that support them
align with other observations of positive relationships between general, self-rated indices of
health and OFTP found in the literature (e.g., Gielnik et al., 2012; Zacher & Frese, 2011).
Job characteristics. In addition to individual characteristics and personal resources,
research suggests that various situational factors may also be related to OFTP. We consider four
job characteristics (i.e., work hours, job demands, job complexity, and job autonomy) that have
been studied in relation to OFTP in our meta-analysis (see Figure 1). While work hours entail
how much employees work, job demands additionally involve the amount of work that has to be
completed within that time (Spector & Jex, 1998). As with gender, there is no strong theoretical
guidance from this literature to support relationships between OFTP and these constructs. On the
one hand, a high number of work hours and high job demands may suggest that employees are
highly invested in their job, which could result in an enhanced OFTP. On the other hand, these
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job characteristics may be considered stressors that lead to reduced OFTP, because employees
cannot imagine an expansive occupational future given their current job conditions (see Barbieri
et al., 2015; Ho & Yeung, 2016). There currently is scant evidence available that directly and
unanimously speaks to positive or negative associations between OFTP and these constructs.
Job complexity and job autonomy are typically considered work-related resources in the
OFTP literature (Zacher & Frese, 2009). Job complexity refers to the extent to which the tasks in
a job are complex and challenging (Morgeson & Humphrey, 2006), whereas job autonomy
(sometimes referred to as job control) involves “the degree to which the job provides substantial
freedom, independence, and discretion to the individual in scheduling the work and in
determining the procedures to be used in carrying it out” (Hackman & Oldham, 1976, p. 258).
Jobs characterized by high complexity and control require that employees use their knowledge,
skills, and abilities, and learn continuously (Kozlowski & Hults, 1986), resulting in better mental
health (Caplan, Cobb, French, Van Harrison, & Pinneau, 1975) and higher work motivation
(Hackman & Oldham, 1976). In general, research finds that both job complexity and job
autonomy are positively related to OFTP (e.g., Zacher & Frese, 2009). One argument for this
observation based on conservation of resources theory (Hobfoll & Wells, 1998) suggests that
jobs with higher complexity and autonomy offer resource-rich work contexts, which help
employees to gain additional resources in terms of perceived remaining time and focus on
opportunities (see also Zacher et al., 2010; Zacher & Frese, 2009, 2011). In addition, Zacher and
colleagues argue that individuals use their perceptions of current work situations to draw
inferences about their future work (cf. Markus & Nurius, 1986; Markus & Wurf, 1987),
suggesting that a current resource-rich work environment will lead to positive perceptions about
future work environments and, thus, higher OFTP.
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Outcomes of Occupational Future Time Perspective
Past research has focused on two broader categories of important work-related outcomes
of OFTP. The first category includes indicators of work attitudes, motivation, and occupational
wellbeing (i.e., job satisfaction, organizational commitment, work engagement, emotional
exhaustion, retirement intentions, work continuance intentions, achievement motivation, and
learning motivation), whereas the second category includes task and contextual performance.
Job attitudes, motivation, and wellbeing outcomes. The literature on associations
between OFTP and favorable job attitudes, motivations, and wellbeing outcomes (e.g., Schmitt,
Zacher, & de Lange, 2013) tends to focus on the importance of positive future thinking to
support such relationships (Oettingen & Mayer, 2002). In his theory, Nuttin (1964) posited that
FTP influences the valence of future outcomes. Similarly, de Volder and Lens (1982) distinguish
between cognitive and affective aspects of FTP, arguing that individuals who score high on
affective aspects of FTP have a more optimistic outlook on the future, have higher levels of
confidence in the attainment of future goals, and attach greater value to future rewards.
Optimistic thinking, in turn, is associated with successful cognitive and self-regulatory problem
solving, prosocial and helping behavior, setting high standards and aspirations, and indicators of
mental health, which are all essential for favorable attitudes, motivation, and wellbeing
(Oettingen & Mayer, 2002).
Similarly, Seligman and Csikszentmihalyi (2000) argue that future mindedness is
beneficial for wellbeing, because it is a positive individual difference characteristic that can act
as a buffer against mental illness and improve quality of life. Building upon this line of
reasoning, primary studies have found generally positive associations between OFTP and the
constructs of job satisfaction (e.g., Weikamp & Göritz, 2016), organizational commitment (e.g.,
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Profili, Sammarra, & Innocenti, 2017), work engagement (e.g., Schmitt, Zacher, & de Lange,
2013), continuance intentions (e.g., Chen, 2015), achievement motivation (e.g., Froehlich et al.,
2016), and motivation to learn (Kochoian et al., 2017). Additionally, negative relationships have
been found between OFTP and emotional exhaustion (e.g., Barbieri et al., 2015) and intentions
to retire (e.g., Bal, de Lange, et al., 2015). To further codify the nature of these findings, we
synthesize all of these relationships in our meta-analysis.
Job performance outcomes. Research has demonstrated positive relationships between
OFTP and various performance-related outcomes. Such studies tend to focus on the theory of
possible selves (Markus & Nurius, 1986) and on self-regulation theory (Bandura, 2006; Miller &
Brickman, 2004) to explain these associations (e.g., Gielnik et al., 2012; Kochoian et al., 2017;
Zacher et al., 2010). According to Cross and Markus (1991), possible selves provide self-
relevant goals and opportunities and thereby the essential link between individuals’ cognitions
and motivation. Similarly, Janeiro (2010) argued that thinking about the future allows people to
motivate themselves and guide their actions in anticipation of future events; as such, the
cognitive ability to plan and organize future activities is an important self-regulatory mechanism
to motivate employees (Miller & Brickman, 2004). Employees who perceive a long occupational
future filled with new goals and opportunities will set proximal subgoals to link their current
efforts to attain these distal goals and opportunities. Following from these arguments, research
has demonstrated that employees with high levels of OFTP tend to perform better at work (e.g.,
Weikamp & Göritz, 2016; Zacher et al., 2010), both in terms of the proficiency of task-relevant
behavior (i.e., task performance) and in terms of helping others and their organization (i.e.,
contextual performance; Borman & Motowidlo, 1993).
Distinguishing OFTP from Related Developmental Constructs
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Beyond holding favorable perceptions of the occupational future (i.e., OFTP), the use of
action regulation strategies (i.e., SOC) is another important developmental regulation mechanism
to consider for the prediction of work outcomes. Like OFTP, the SOC construct emerged from
the lifespan developmental literature (Baltes & Baltes, 1990). SOC refers to the orchestration of
a set of three interrelated and complimentary behavioral strategies, selection, optimization, and
compensation, which serve important goal regulation functions (Freund & Baltes, 2000; 2002).
Selection may take either an elective form, referring to the extent to which individuals set and
prioritize new goals to achieve desired states, or a loss-based form, referring to the extent to
which individual disengage from unattainable goals (e.g., via selecting new goals or reorganizing
goal priorities). Optimization refers to the allocation and investment of personal resources (e.g.,
time, effort, and knowledge) in service of goal attainment. Finally, compensation refers to those
actions that, in the face of resource losses, aid in the acquisition of new resources, or the re-
activation of unused resources, to achieve one’s goals. As a whole, SOC strategy use is
particularly important to successful developmental outcomes when demands outweigh resources,
and the SOC model proposes that people who experience a mismatch between their demands and
resources can maintain effective functioning and wellbeing by using SOC strategies (Baltes &
Baltes, 1990).
Research has previously considered empirical links between OFTP and SOC. For
example, Zacher and Frese (2011) found that focus on opportunities at work was positively
related to SOC, and this effect was not conditional upon job characteristics (i.e., job complexity).
More recently, Baltes et al. (2014) reported longitudinal links between domain-general future
time perspective (i.e., assessed via the scale by Carstensen and Lang, 1996) and SOC. Consistent
with the pattern reported by Zacher and Frese (2011), this study suggested that, over time, future
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time perspective was positively related to SOC. This suggests that employees who focus on and
successfully pursue important work goals also perceive more remaining time and work-related
opportunities in the future. Consistent with the assumption that SOC strategy use helps
employees to invest their personal resources in an optimal way at work, a meta-analysis showed
that SOC strategy use is associated with favorable work outcomes, including job satisfaction,
work engagement, and job performance (Moghimi et al., 2017). Moreover, Moghimi and
colleagues (2017) showed that SOC strategy use is weakly, yet positively, associated with age.
In our meta-analysis, we examine whether OFTP predicts work outcomes above and
beyond (i.e., incremental to) SOC strategy use. In addition, as age is strongly and negatively
correlated with OFTP, we follow recommendations in the literature (Schmitt, Zacher, & de
Lange, 2013) and control for age when using OFTP to predict work outcomes. Finally, age has
also been shown to be associated with various work outcomes in past research (Ng & Feldman,
2008, 2009), and as both OFTP and SOC are related to age, we consider how age is indirectly
related to work outcomes through OFTP and SOC. To this end, Rudolph (2016) has argued that
more integrative tests of multiple developmental constructs should be undertaken, suggesting
that OFTP might work in tandem with SOC within a larger goal striving action-phase sequence.
Our meta-analytic review of these constructs is well geared to empirically “unpack” some of the
complexities among these constructs that have been noted in this literature.
Method
Literature Search
Best practices for the conduct of meta-analyses dictate the need to complete thorough and
comprehensive literature searches (e.g., Cooper, Hedges, & Valentine, 2009; Higgins & Green,
2011), and to exhaust all efforts to obtain published and unpublished studies to circumvent the
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possibility of publication bias (McDaniel, Rothstein, & Whetzel, 2006) stemming from the so-
called “file drawer problem” (Rosenthal, 1979). As such, we conducted a comprehensive
literature search between September 1, 2016 and March 1, 2017 aimed at obtaining both
published and unpublished primary studies. We also conducted a supplemental search of this
literature in September of 2017 to support a revision effort. As a first search strategy, we
searched the electronic search engine Google Scholar, which yielded the highest initial search
based upon our keywords. After collecting relevant studies from this first search, we then
conducted iterative follow-up searches using various search engines and databases, including
EBSCOHost, Emerald, JSTOR, ProQuest, PsycINFO, ScienceDirect, and Web of Science. For
each subsequent search engine and database, we collected all non-redundant studies (i.e., those
that were uniquely identified as not overlapping with previous searches). Given that the original
OFTP scale was published by Zacher and Frese (2009), all studies included in our meta-analysis
had likewise been published or otherwise conducted since 2009.
The literature searches used the keyword “occupational future time perspective” as well
as the individual dimensions of OFTP as defined by Zacher and colleagues (i.e., “perceived
remaining time” and “focus on opportunities,” Zacher & Frese, 2009; “focus on limitations,”
Zacher, 2013). We additionally conducted ancillary searches for specific OFTP scale items as
keywords (e.g., "Most of my occupational life lies ahead of me" OR "My occupational future
seems infinite to me" OR "As I get older, I begin to experience time in my occupational future as
limited" OR “Many opportunities await me in my occupational future” OR “I expect that I will
set many new goals in my occupational future” OR “My occupational future is filled with
possibilities”).
To be even more comprehensive, we conducted “snowball” searches to find all studies
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citing the original Zacher and Frese (2009) scale development paper. To locate additional
studies, we further examined the references of all qualifying primary studies and conducted
forward searches of those relevant studies that cited each retrieved article. In total, this
exhaustive search process yielded an initial set of over 150 references. Based upon our a priori
inclusion and exclusion criteria (see below), we collected only those relevant quantitative and
empirical studies of OFTP from the initial studies obtained by carefully examining the abstract,
methods, and results of each study.
To supplement our initial literature searches, we also cross-referenced conference
programs from the Academy of Management (2010-2015), the Society for Industrial and
Organizational Psychology (2010-2016), and the European Association for Work and
Organizational Psychology (2011, 2013, & 2015). Finally, in an attempt to obtain unpublished
data, manuscripts in preparation, and in-press articles, we sent personal emails to 20 researchers
who have published previously on OFTP. We also put out formal calls for unpublished data via
professional mailing lists and website postings. Lastly, we searched for pre-press “online first”
articles via various relevant journals that have previously published OFTP studies (e.g., Journal
of Organizational Behavior; Journal of Vocational Behavior; Work, Aging and Retirement).
After these efforts, our primary meta-analytic database contained 406 effect sizes coded
from K = 38 sources. Two studies (Kooij & Zacher, 2016; Schmitt, Zacher, & de Lange, 2013)
report results from two separate samples, thus our database represents K = 40 independent
samples and a total of N = 19,112 workers. Our secondary meta-analytic database of the
intercorrelations between the Zacher and Frese (2009) OFTP dimensions was based upon a total
of K = 16 independent samples, representing a subset of N = 7,549 workers. All studies included
in our meta-analysis are indicated with an asterisk (i.e., *) in the reference list. Figure 2 outlines
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the flow of this literature search process, including a specification of the intermediate yields of
included and excluded studies that resulted in our final database of K = 40 independent samples.
While coding primary studies, we took proactive efforts to contact authors to clarify
information (e.g., the dummy coding pattern of gender; type of tenure) or missing data (e.g.,
scale reliabilities; intercorrelations among OFTP dimensions). In general, such issues were easily
rectified (i.e., in no cases were we unable to receive the required information for inclusion of a
given relationships).
Inclusion and Exclusion Criteria
As part of our larger research effort, we conducted two separate meta-analyses. The first
primary meta-analysis considers overall OFTP and specific OFTP dimension relationships. The
second supporting meta-analysis considers interrelationships among these OFTP dimensions.
Because the goals of these two meta-analyses were somewhat different, we initially developed
and applied two sets of a priori inclusion and exclusion criteria. For the primary meta-analysis,
we set seven specific inclusion and exclusion criteria to guide our literature searches.
First, to be included, studies must have measured OFTP in terms of either (a) perceived
remaining time or focus on opportunities as outlined by Zacher and Frese (2009), or in terms of
focus on limitations as outlined by Zacher (2013) or (b) as overall OFTP, an aggregation of two
or more of these dimensions. Studies adopting alternative measurement instruments (i.e., those
using domain-general FTP scales in the work context, e.g., Kooij & van de Voorde, 2011) were
excluded from our analysis. In terms of conceptualizing overall OFTP in our analyses, we either
coded such relationships directly from studies that included OFTP as a composite score (e.g., Ho
& Yeung, 2016) or we computed a composite score to represent overall OFTP across the
dimension-level correlations using Hunter and Schmidt’s (2004) composite formulae. This first
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inclusion criterion led to the exclusion of review articles (e.g., Henry et al., 2017) and studies
adopting qualitative methodologies (e.g., Ng & Law, 2014).
Second, in addition to measuring OFTP, at least one of the individual characteristics, job
characteristics, or work outcomes from our integrative model must also have been measured (see
Figure 1). Third, we were very careful to only code independent effect sizes from each primary
study so as not to “double count” studies. This was a particular concern as we sought to include
unpublished bachelor’s and master’s theses and doctoral dissertations in our meta-analysis (K =
13). Another related concern regarding student works is that, in some universities, groups of
bachelor’s and master’s students work together in “thesis circles” to complete such projects. We
identified K = 1 thesis circle that qualified for inclusion here (Mauritz, 2012; van der Maarel,
2011). In this case, we only coded independent and non-overlapping relationships that were
unique to each individual study.
Fourth, whenever longitudinal analyses were reported, we coded relationships based on
time-one data for complete panel designs (e.g., Kooij & Zacher, 2016), and between OFTP and
relevant correlates at other time points when incomplete panel designs were used (e.g., Weikamp
& Göritz, 2016). Fifth, whenever studies reported results from multiple independent samples,
each sample was included as a separate independent study in our meta-analysis (e.g., Schmitt,
Zacher, & de Lange, 2013).
Sixth, for studies that adopted intensive longitudinal designs (i.e., so-called experience
sampling or daily-diary studies), we considered only between-person effects to be consistent
with our operationalization of OFTP (i.e., within-person data aggregated to the between-person
level of analysis; e.g., Schmitt, Zacher, & de Lange, 2013). Finally, studies reporting results in
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languages other than English were translated using translation software and native speakers (i.e.,
Dutch, German).
For the secondary meta-analysis of OFTP dimension-level intercorrelations, we assumed
an eighth inclusion criterion. Specifically, we additionally sought to quantify the strength of the
intercorrelations between individual dimensions of OFTP. For this analysis, we only considered
studies that measured the two OFTP dimensions included in the Zacher and Frese (2009) OFTP
scale (i.e., “perceived remaining time” and “focus on opportunities”). Indeed, while we
originally sought to include “focus on limitations,” too few studies utilized this dimension from
Zacher (2013) to be included here (K = 1).
Measures of Key Constructs
Our meta-analysis considered relationships of overall OFTP and its dimensions with a set
of individual characteristics, job characteristics, and work outcomes (Figure 1). Consistent with a
great deal of past research and methodological best practices for the conduct of meta-analyses,
we included such relationships in our models in cases where they were represented in at least
three (K 3) independent samples. As outlined by Valentine, Pigott, and Rothstein (2010), even
when K = 2, meta-analysis is superior to other means of synthesis (e.g., the so-called “cognitive
algebra” by which one tries to mentally integrate multiple findings across studies). Moreover, a
number of previous meta-analyses in the organizational sciences have successfully adopted this
K 3 criterion (e.g., Choi, Oh, & Colbert, 2014; Eby, Allen, Evans, Ng, & DuBois, 2008; Kirca,
Hult, Deligonul, Perryy, & Cavusgil, 2012; Viswesvaran & Ones, 1995; Viswesvaran, Schmidt,
& Ones, 2002). In terms of the application of this criterion, 14.04% of the effects sizes computed
herein were based upon K = 3 effect sizes, and the average number of studies defining a given
zero-order meta-analytic effect reported here is approximately K = 7.
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When overlapping variables were not available in at least three samples, we logically
combined them into a typology of synthetic construct groupings. This was the case for nine
variables considered here. Table 1 summarizes the specific operationalizations of the variables
for each synthetic construct grouping. Additionally, when coding effect sizes for individual
characteristics, age and tenure were conceptualized chronologically (i.e., in years). Furthermore,
we considered both job (e.g., Ho & Yeung, 2016) and organizational (e.g., Barbieri et al., 2015)
tenure separately in our analysis. Gender was operationalized as a dummy coded variable, such
that higher values were indicative of females (i.e., 0 = male, 1 = female). Educational level was
operationalized in terms of level of accomplishment, such that higher scores indicate higher
levels of educational attainment. Finally, work hours were conceptualized in terms of continuous
time worked (i.e., higher = more work hours/week). A table in the Appendix outlines those
constructs coded from the K = 38 studies considered here.
Meta-Analytic Procedure
Following our comprehensive literature search, the first and third authors worked
together to complete the coding of primary studies by applying the a priori determined inclusion
and exclusion criteria outlined above. Coding correlations and reliabilities directly from primary
studies is a “low inference” process (Cooper, 1998, p. 30) that does not require subjective
judgments (Hunter & Schmidt, 2004; Whetzel & McDaniel, 1988). Accordingly, there were very
few disagreements encountered during the coding process. Additionally, the coding team held
weekly calibration meetings, and the few disagreements encountered during such meetings were
discussed until agreement was reached via consensus.
While several approaches to meta-analysis exist, we followed Hunter and Schmidt’s
(2004) methods. These procedures allow for the correction of observed correlations for sampling
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and measurement errors, and combine effect size estimates using random-effects estimation
procedures. As a first step, this procedure corrects for sampling error by calculating sample size-
weighted correlations. Second, where possible (i.e., for multi-item scales), corrections for the
lack of perfect reliability are applied, as it is well-established that unreliability attenuates zero-
order correlations (Hunter & Schmidt, 2004). To accomplish these corrections, artifact
distributions were constructed and applied for cases in which a study did not report the reliability
estimate for a given construct (Hunter & Schmidt, 2004).
Beyond the sample-size weighted correlation (rbar) and the sample size-weighted and
reliability-corrected correlation (rho, ρ), we computed 95% confidence intervals and the 80%
credibility interval for each ρ, as well as the percent of variance in ρ that is attributable to
statistical artifacts (% var). A sample size-weighted and reliability-corrected correlation is
considered to be statistically significant when its associated confidence interval does not include
zero. If an 80% credibility interval includes zero, this may indicate the presence of moderators
(Geyskens, Krishnan, Steenkamp, & Cunha, 2009). Alternatively, Hunter and Schmidt (2004)
offer the “the 75% rule(i.e., a moderator is likely to be present when the percentage of variance
accounted for by statistical artifacts is < 75%).
Results
Table 2 contains the results of the primary meta-analysis of zero-order correlations
between OFTP and its antecedents and outcomes as defined by our model (see Figure 1). Table 3
summarizes the supplementary meta-analysis of intercorrelations among OFTP dimensions.
Because of the relatively large number of zero-order relationships considered in our primary
analysis, we largely focus our summary of these results on the overall OFTP relationships, unless
such relationships were not represented in the literature (i.e., as was the case for organizational
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commitment and learning motivation, which were represented only by specific OFTP
dimensions). Beyond the associations involving overall OFTP and other constructs considered
here, it is also important to recognize that in a number of cases, notably stronger relationships
(i.e., in terms of their absolute magnitude and the amount of variance accounted for in a bivariate
sense) were observed for specific OFTP dimensions. Thus, as relevant, we additionally
summarize notable differential dimension-level relationships. Unless otherwise noted, the
relationships reported next were statistically significant (p < .05).
Relationship between Dimensions of Occupational Future Time Perspective
Consistent with past research (e.g., Froehlich et al., 2016; Weikamp & Göritz, 2015), the
meta-analysis of intercorrelations between OFTP dimensions (see Table 3) suggests that
perceived remaining time and focus on opportunities are strongly and positively correlated (ρ =
0.72).
Antecedents of Occupational Future Time Perspective
Individual characteristics and personal resources. Age (ρ = -0.55), job tenure (ρ = -
0.23), and organizational tenure (ρ = -0.25) were all negatively related to OFTP. Age was more
strongly related to perceived remaining time (ρ = -0.61) than focus on opportunities (ρ = -0.34),
explaining over twice the variance in OFTP (i.e., 37.58% vs. 11.83%, respectively). Educational
level was positively associated with OFTP (ρ = 0.16). Likewise, self-rated physical health was
positively related to OFTP (ρ = 0.16). With respect to the relationship between OFTP and
gender, there was evidence for a small yet significant gender difference in OFTP (ρ = 0.05),
suggesting that women have a slightly more expansive OFTP than men. However, this only
holds for perceived remaining time, and should be interpreted with caution given critiques of the
implications of such gender effects in meta-analytic reviews (e.g., Hyde, 2005).
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Job characteristics. Job complexity (ρ = 0.03) and job autonomy (ρ = 0.15) were both
positively related to OFTP. Importantly, job autonomy was more strongly related to focus on
opportunities (ρ = 0.24) than perceived remaining time (ρ = 0.11), explaining over four times
more variance in OFTP (5.76% vs. 1.25%). Considering the relationships with job
characteristics, there was a small positive association observed between OFTP and work hours (ρ
= 0.10) and a non-significant association observed between OFTP and job demands (ρ = 0.01,
95% CI: -.07 to .08).
Outcomes of Occupational Future Time Perspective
Job attitudes, motivation, and wellbeing outcomes. OFTP was associated with higher
job satisfaction (ρ = 0.28), work engagement (ρ = 0.22), work continuance intentions (ρ = 0.15),
and achievement motivation (ρ = 0.20). Job satisfaction was more strongly related to focus on
opportunities (ρ = 0.40) than perceived remaining time (ρ = 0.15), explaining nearly seven times
more variance in OFTP (16.08% vs. 2.34%). Similarly, work engagement was more strongly
related to focus on opportunities (ρ = 0.34) than perceived remaining time (ρ = 0.12), explaining
nearly eight times more variance in OFTP (11.28% vs. 1.41%). OFTP was also associated with
lower retirement intentions (ρ = -0.37) and lower emotional exhaustion (ρ = -0.19). Although we
also considered organizational commitment, studies that included this outcome have only
measured focus on opportunities (ρ = 0.41). Likewise, we also considered learning motivation,
but studies that included this outcome have only considered perceived time remaining (ρ = 0.38).
Job performance outcomes. OFTP was positively associated with task performance (ρ
= 0.11) and contextual performance (ρ = 0.20). At the dimension level, only focus on
opportunities was significantly associated with task performance (ρ = 0.12). Focus on
opportunities was more strongly related to contextual performance (ρ = 0.28) than perceived
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remaining time (ρ = 0.13), explaining nearly five times more variance in OFTP (7.90% vs.
1.59%).
Related lifespan developmental constructs. OFTP was positively related to SOC
strategy use (ρ = 0.15). Focus on opportunities was more strongly related to SOC strategy use (ρ
= 0.19) than perceived remaining time (ρ = 0.09), and explained approximately four times more
variance in OFTP (3.42% vs. .08%).
Meta-Analytic Regression and Path Analysis Models
We further explore whether OFTP predicts important work outcomes above and beyond
the effects of employee age and SOC strategy use. We conducted a series of regression and path
analyses based upon a constructed meta-analytic correlation matrix (see Table 4) to test the
unique relationships of OFTP against age and SOC strategy use. To facilitate testing these
models, we focused on the four outcomes that were investigated in both the present manuscript
and the recent Moghimi et al. (2017) meta-analysis of SOC strategy use relationships (i.e., job
satisfaction, work engagement, emotional exhaustion, and task performance).
Previous meta-analytic evidence supports relationships between age and three of these
four outcomes (i.e., job satisfaction, Ng & Feldman, 2010; task performance, Ng & Feldman,
2008; emotional exhaustion, Brewer & Shapard, 2004). However, there has not as-of-yet been a
meta-analysis of the work engagement literature that has considered age–engagement
relationships. To support this analysis, we conducted a bare-bones meta-analysis of such
relationships (K = 31; N = 26,751; r = 0.12, p < .05) via the MetaBus database (Bosco, Steel,
Oswald, Uggerslev, & Field, 2015). We searched this database for the keywords “age” and
“work engagement.” This search initially yielded K = 35 studies, of which four studies were
excluded because they were duplicate records. This ad hoc analysis allowed us to complete this
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missing cell of the meta-analytic correlation matrix and thus specify these models. Additionally,
because the results of our meta-analysis of the relationship among OFTP dimensions suggested a
strong association between perceived remaining time and focus on opportunities, we specified
such models with overall OFTP relationships rather than these two dimensions to avoid issues
associated with multicollinearity. Finally, as suggested by Viswesvaran and Ones (1995), the
sample size for each regression model was the harmonic mean of the sample size across the
relevant correlations considered.
To support conclusions about the unique predictive role of OFTP in these models, we
also conducted relative weights analyses (see Johnson, 2000). When predictors are correlated,
the relative contribution of each to the model R2 cannot be determined by examining the partial
regression weights alone (LeBreton, Ployhart, & Ladd, 2004). Relative weights analysis
computes both relative weights and rescaled relative weights: relative weights reflect the
proportion of variance explained in an outcome that is attributed to each of the predictors,
whereas the rescaled relative weights reflect the percentage of explained variance that is
accounted for by each predictor variable (i.e., calculated by dividing the relative weights by the
model R2; LeBreton, Hargis, Griepentrog, Oswald, & Ployhart, 2007).
A summary of formal tests of incremental effects of OFTP above-and-beyond age and
SOC (i.e., in terms of change in R2) can be found in Table 5. To address such effects, we first
regressed each outcome onto age and SOC on step one of a hierarchical regression model, and
then included OFTP on step two. Changes in variance explained (ΔR2) between these two models
are indexed by a significant Fpartial, which would suggest that OFTP explains an appreciable
amount of additional variance compared to the model that solely specifies the effects of age and
SOC. Of note, OFTP additionally accounted for between 1.27% and 13.99% of the variance in
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outcomes above and beyond age and SOC. Table 6 summarizes each “step two” model
referenced above, including specific parameters for age, SOC, and OFTP, and raw and rescaled
relative weights.
Additionally, speaking to the incremental role of OFTP, the model term representing the
effect of OFTP was statistically significant (p < .001) in each model depicted in Table 6.
Together, this evidence suggests that OFTP is incrementally important when considered in
tandem with age and SOC. Beyond the statistical significance of OFTP in these models, the
relative weights analyses reported in Table 6 suggest that OFTP accounts for an appreciable
amount of the variance observed in job satisfaction (%R2 = 48.34%), emotional exhaustion (%R2
= 51.00%), and work engagement (%R2 = 50.64%). However, SOC was a more important
predictor of task performance (%R2 = 63.64%) than OFTP (%R2 = 21.27%).
We further conducted a meta-analytic path model to test the competing effects of SOC
and OFTP as mediators of the relationship between age and the same four outcomes. Recent
developments concerning successful aging at work have called for the testing of process models
that include age-related mediators, such as OFTP and SOC (Zacher, 2015). Thus, this model
represents a novel test of the notion of successful aging with meta-analytic data. Table 7
summarizes model parameters, and Table 8 summarizes indirect effects and Monte Carlo
confidence intervals (Preacher & Selig, 2012) of age on the four outcomes through OFTP and
SOC. The model fits the data well (Chi-Square = 153.11, p < .001; CFI = .98; SRMR = .03). Age
was associated with lower OFTP (B = -0.52, R2 = .27) and somewhat higher SOC (B = 0.04, R2
= .001). Consistent with the results of the multiple regression analyses, OFTP was associated
with lower emotional exhaustion, as well as higher job satisfaction, work engagement, and task
performance.
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Extending these results, this model further suggests that OFTP partially mediates all of
the pathways between age and the four outcomes when controlling for the parallel effects of
SOC. Past meta-analytic evidence suggests that age is associated with lower levels of emotional
exhaustion (Brewer & Shapard, 2004), and higher levels of job satisfaction (Ng & Feldman,
2010) and task performance (Ng & Feldman, 2008). Furthermore, our ad hoc meta-analysis
suggests that age is positively related to work engagement. However, these meta-analytic
relationships are all of a modest magnitude. The inconsistent indirect effects of age on these
outcomes through OFTP may account for these modest relationships (MacKinnon, Fairchild, &
Fritz, 2007). Taken together, these results suggest that OFTP is an age-related mediator for
explaining variance in these important work outcomes.
Discussion
Our primary goal with this meta-analysis was to examine the nomological network of
associations between OFTP and individual and job characteristics, as well as various important
work outcomes. In addition, we aimed to examine the unique predictive validity of OFTP above
and beyond chronological age and SOC strategy use, and to examine the indirect associations of
age with work outcomes through OFTP. We found that age, as well as job and organizational
tenure, are negatively associated with OFTP. Moreover, educational level, self-rated physical
health, number of work hours, job complexity, and job autonomy were positively associated with
OFTP. We further found that OFTP has positive associations with job satisfaction, work
engagement, work continuance intentions, and achievement motivation, as well as task and
contextual performance. In contrast, OFTP was negatively related to retirement intentions and
emotional exhaustion. Organizational commitment was only positively associated with focus on
opportunities, and learning motivation was only positively associated with remaining time;
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studies considering these outcomes only measured focus on opportunities and remaining time,
respectively. We further found that OFTP was associated with four outcomes (i.e., job
satisfaction, work engagement, emotional exhaustion, and task performance) above and beyond
the effects of chronological age and SOC strategy use, and that OFTP partially mediates the
associations between age and these outcomes when statistically controlling for parallel effects of
SOC strategy use.
Theoretical Contributions
With our meta-analysis we contribute to the organizational behavior literature in several
ways. First, we demonstrate that OFTP, a temporal construct, is associated with work
engagement, task performance, and retirement intentions. These are important work outcomes in
a contemporary society in which longer working lives are becoming the norm. Consistent with
theories on possible selves (Markus & Nurius, 1986), positive future thinking (Oettingen &
Mayer, 2002), and self-regulation (Bandura, 2006), we found that individuals with a positive
view on their occupational future have more positive job attitudes, higher motivation and
wellbeing, and better job performance. Individuals with high OFTP seem to be optimistic people
with a clearer image of their future selves, and thus they may be more likely to set high standards
and aspirations, as well as to engage in successful cognitive and self-regulatory problem solving
and behavior aimed at reaching future goals (Oettingen & Mayer, 2002).
Second, we examined potential antecedents of OFTP to gain insight into the individual
and job characteristics that may be associated with OFTP. We demonstrate that particularly other
time-related factors, such as age, job tenure, and organizational tenure, are related to OFTP. The
older employees are and the more time they have spent in their jobs and organizations, the
shorter they perceive their remaining time and the more constraints they perceive for their future
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opportunities at work. Further, consistent with propositions of Hobfoll’s (1989) conservation of
resources theory, we found that individual resources, such as higher educational level and self-
related physical health, as well as job resources, such as high levels of job autonomy and
complexity, seem to help employees maintain higher levels of perceived remaining time and
focus on opportunities.
Third, we contribute to the growing literature on successful aging at work (Zacher, 2015)
by demonstrating that OFTP mediates associations between age and work outcomes. Similar to
SOC strategy use, OFTP can be considered as a lifespan developmental regulating mechanism;
while age is generally negatively related to OFTP, those who maintain favorable perceptions of
their occupational future seem to be more likely to stay engaged and healthy and to perform well
at work. In contrast, older workers with low OFTP are more likely to experience lower work
engagement, more emotional exhaustion, and poorer performance. Recent theoretical
developments regarding the notion of successful aging at work have called for the testing of
process models that include age-related mediators (Zacher, 2015). Answering this call, we reveal
that OFTP explains why some work outcomes may decrease with age. Moreover, our findings
show that OFTP is a unique developmental mechanism that has incremental predictive validity
above and beyond age and SOC strategy use. This finding answers another recent call in the
literature on work and aging to consider competing lifespan developmental mechanisms
simultaneously (Rudolph, 2016). Specifically, our results suggest that OFTP is a unique entity
within the larger nomological network of developmental constructs that may be relevant for the
work context.
Finally, although research on OFTP typically distinguishes between perceived remaining
time and focus on opportunities as two distinct dimensions of OFTP, we found that these
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dimensions are strongly and positively associated with one another. This finding may suggest
that these two dimensions could be combined in future research into an overall OFTP score.
Observing the overlap between confidence intervals displayed in Table 2, we do note that there
are notable differences in the strength of the relationships between these two OFTP dimensions
and several variables included in our meta-analysis (e.g., age, job autonomy, job satisfaction,
work engagement). However, as these comparisons are not necessarily independent, some
caution should be exercised in interpreting these differences.
Practical Implications
In addition to these theoretical contributions, our findings have a number of practical
implications. First, we demonstrate the importance of perceiving a long occupational future filled
with opportunities for organizations (e.g., task performance), individual employees (e.g., work
engagement), and governments and societies (e.g., retirement intentions). The OFTP construct
should thus be high on the agendas of both organizations and governments. These findings are
even more important considering the rapidly aging workforce in most countries around the globe
and the finding that OFTP mediates associations of age with important work outcomes. By
extending workers’ OFTP, organizations and governments can help them age more successfully
at work. In addition, our work provides HR managers with ideas on how to extend OFTP. One
possible strategy could be to redesign jobs such that jobs become more autonomous and complex
(i.e., challenging). Another strategy is to improve worker physical health, which could be
accomplished by implementing vitality programs.
Limitations and Future Research Agenda
Our meta-analysis has considerable strengths, but, nevertheless, we also acknowledge
certain limitations of this work. First, we were only able to include variables in our meta-analysis
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that have been considered in past empirical research. This particularly limited our insight into
antecedents of OFTP. Although we found that a number of individual and job resources are
positively associated with OFTP, other individual resources, such as certain personality traits (cf.
Zacher & Frese, 2009) or socioeconomic status, and other job resources, such as supervisor
support or opportunities for development, could be important antecedents of OFTP as well. In
addition to these variables, future research could examine the role of organizational level factors,
such as HR practices (e.g., career planning and vitality programs) or the climate within the
organization (e.g., age-diversity climate, Boehm, Kunze, & Bruch, 2014; see also Zacher &
Yang, 2016), and constraining job factors, such as hindering job demands and negative life
events. Related to this, although the OFTP construct is grounded in socioemotional selectivity
theory, which posits age-related dynamics in emotion regulation capacities across the lifespan
(Carstensen, 1991, 2006), little to no research has investigated links between OFTP and emotion
regulation at work. Accordingly, future research must endeavor to test such links.
Second, most studies that are included in our meta-analysis used research designs with
self-reports to measure OFTP, antecedents, and work outcomes, which can potentially lead to
common method bias. Future studies should include more objective measures, such as supervisor
or colleague ratings, particularly to measure job performance outcomes. Third, our meta-analysis
is inconclusive about the role of OFTP dimensions. As noted earlier, future researchers could
consider combining perceived remaining time and focus on opportunities into an overall OFTP
score, as our meta-analysis showed that these two dimensions are strongly correlated. On the
other hand, differences in the strength of the relationships of the two OFTP dimensions with
several variables also suggest that future research would be well served to focus on predictions
related to these specific dimensions, rather than solely upon the overall conceptualization of
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OFTP. In addition, it would be important to clarify the role of a third OFTP dimension, focus on
limitations, that has so far only been investigated in few studies within (Zacher, 2013) and
outside of the work context (Cate & John, 2007).
Fourth, since most studies included in our meta-analysis used cross-sectional (i.e., single
time point) research designs, we cannot draw any conclusions about causality. Although we
propose that individual and job characteristics, such as self-rated physical health and job
autonomy, are antecedents of OFTP, it is possible that OFTP serves as a resource to obtain other
resources such as self-rated health and greater autonomy at work. Likewise, although we propose
that OFTP predicts worker outcomes, such as work engagement and job performance, it might be
that employees who are more engaged or perform better at work create and receive more
opportunities at work, hence increasing their OFTP. To address these limitations, researchers
should conduct intervention studies and use longitudinal research designs to be able to draw
conclusions about causal relationships between OFTP and its potential antecedents and
outcomes. Intervention studies are important, as they would allow us to examine whether it is
possible to develop workshops or trainings that enhance employees’ OFTP. Studies in the
lifespan developmental literature on general FTP have shown that FTP can be manipulated (e.g.,
Fung, Carstensen, & Lutz, 1999). Considering this, future studies could examine whether OFTP
can be manipulated experimentally as well.
Furthermore, longitudinal studies are important to capture the development of OFTP over
time. Conducting longitudinal research across the adult lifespan is both a costly and time-
intensive endeavor and, therefore, it is perhaps not surprising that so few studies adopt such
research designs in the literature on work and aging (see Ng & Feldman, 2008). Indeed, of the
studies included in our meta-analysis, only two adopted multi-wave designs (Kooij & Zacher,
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2016; Schmitt, Gielnik, Zacher, & Klemann, 2013), and only one a true longitudinal design with
more than two measurement waves (Weikamp & Goritz, 2015). To overcome the practical
difficulties associated with longitudinal research, future studies may consider how OFTP
changes across shorter periods of time, especially for individuals who are faced with critical
work transitions (e.g., occupational changes, retirement), and how various positive and negative
career events may differentially impact younger, middle-aged, and older workers’ OFTP.
The general reliance on cross-sectional methodologies in this literature is also a limitation
to the interpretation of the path model we present herein (see Maxwell & Cole, 2007; Maxwell,
Cole & Mitchell, 2011). Accordingly, these results must be interpreted with caution, and the
parameters reported for this model are best thought of as summary effects. Despite noted
limitations, tests of process models in meta-analysis are common (e.g., Michel, Mitchelson,
Pichler, & Cullen, 2010), and the relative merits of these procedures have been likewise
supported (e.g., Shadish, 1996; Viswesvaran & Ones, 1995). Future research should attempt
more formal tests of the implied causal process that is represented by our path analysis.
Finally, our relative weights analyses suggest important patterns of differential influence
when contrasting the amount of variance explained by OFTP versus SOC. Stronger relationships
between SOC and task performance (relative to the contributions of OFTP) and stronger
relationships between OFTP and job satisfaction, work engagement, and emotional exhaustion
(relative to the contributions of SOC), point to the potential for separate yet complimentary
performance and wellbeing/motivation enhancing mechanisms. While this is speculative, more
research concerning the dual roles of SOC as a performance facilitating mechanism and OFTP as
a motivational and wellbeing facilitating mechanism is thus warranted on the basis of these
results, and the concomitant evidence from the dual-mediator path model tested herein.
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Conclusion
This meta-analysis examined antecedents and outcomes of OFTP, a temporal construct of
increasing importance in the context of an aging workforce. In line with lifespan and
organizational psychology theories, we found that age, as well as job and organizational tenure,
are negatively associated with OFTP, and that educational level, self-rated physical health,
number of work hours, job complexity, and job autonomy are positively associated with OFTP.
Moreover, OFTP had positive associations with important job attitudes, motivations, and
wellbeing outcomes, such as work engagement and work continuance intentions, and with job
performance outcomes, such as task performance. In addition, we found that OFTP predicted
these outcomes above and beyond the effects of chronological age and SOC strategy use. Finally,
we showed that OFTP partially mediated the associations between age and these outcomes when
controlling for parallel effects of SOC. These findings demonstrate that OFTP is a unique
developmental mechanism and emphasize the importance of OFTP in the work context.
One final observation bears consideration here as well. Our literature search revealed that
the first author of the initial study published on OFTP (Zacher & Frese, 2009) was involved in a
notable proportion of the published and unpublished studies that we included in our meta-
analysis (see Appendix). As our findings suggest that OFTP has meaningful relationships with
several important work outcomes, we believe that the time is ripe for other researchers and
research teams to conduct studies on OFTP to gain an even better understanding of its
nomological network and practical relevance. Thus, as a closing point, we would like to formally
call for such enhanced lines of inquiry into OFTP.
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Table 1. Summary of Synthetic Construct Groupings
Synthetic Construct
Included Operationalizations
Achievement Motivation
Achievement Goal Orientation
Achievement Striving
Growth Motives
Motivation for Job Growth
Need for Achievement
Emotional Exhaustion
Burnout
Emotional Exhaustion
Work Continuance Intentions
Continuance Intentions
Motivation to Continue Working
Self-Rated Physical Health
General Health
Physical Health
Subjective Health
Work Ability
Job Control
Job Autonomy
Job Control
Job Discretion
Learning Motivation
Learning Goal Orientation
Learning Self-Efficacy
Motivation to Learn
Perceived Job Demands
Perceived Job Demands
Perceived Job Stress
Work Pressure
Work Engagement
Job Engagement
Work Engagement
Task Performance
Task Performance
Work Performance
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Table 2. Results of Zero-Order Meta-Analysis
OFTP
K
N
rbar
SD rbar
ρ
SD ρ
CIL
CIU
%Var
CVL
CVU
Overall
22
9,490
-0.519
0.223
-0.549
0.233
-0.650
-0.448
2.99
-0.848
-0.251
FOO
30
15,111
-0.325
0.120
-0.344
0.120
-0.389
-0.298
11.38
-0.497
-0.190
PRT
20
9,068
-0.553
0.206
-0.613
0.224
-0.713
-0.513
3.82
-0.900
-0.325
Overall
4
3,238
-0.212
0.053
-0.225
0.043
-0.280
-0.170
41.44
-0.280
-0.170
FOO
4
3,871
-0.151
0.042
-0.162
0.030
-0.206
-0.118
55.99
-0.200
-0.123
PRT
4
3,120
-0.226
0.049
-0.246
0.037
-0.298
-0.194
51.56
-0.293
-0.198
Overall
8
3,957
-0.232
0.128
-0.249
0.129
-0.349
-0.148
11.52
-0.414
-0.083
FOO
9
8,466
-0.225
0.046
-0.238
0.035
-0.270
-0.207
48.61
-0.283
-0.194
PRT
7
3,523
-0.216
0.199
-0.238
0.214
-0.400
-0.075
4.73
-0.512
0.036
Overall
13
6,260
0.154
0.088
0.162
0.080
0.110
0.214
25.95
0.060
0.264
FOO
16
7,123
0.154
0.061
0.163
0.041
0.132
0.194
58.74
0.110
0.216
PRT
11
6,097
0.101
0.078
0.111
0.072
0.060
0.162
29.18
0.019
0.204
Overall
8
4,439
0.149
0.086
0.162
0.082
0.097
0.227
24.34
0.058
0.267
FOO
8
4,189
0.117
0.081
0.128
0.075
0.067
0.190
29.00
0.032
0.225
PRT
6
3,972
0.133
0.063
0.148
0.055
0.092
0.204
37.86
0.078
0.219
Overall
18
10,078
0.045
0.059
0.048
0.044
0.019
0.077
50.93
-0.008
0.104
FOO
21
12,438
-0.006
0.038
-0.006
0.000
-0.023
0.011
100.00
-0.023
0.011
PRT
15
7,662
0.054
0.064
0.059
0.052
0.023
0.095
47.20
-0.007
0.125
OFTP
K
N
rbar
SD rbar
ρ
SD ρ
CIL
CIU
%Var
CVL
CVU
Overall
4
3,303
0.097
0.057
0.102
0.048
0.043
0.161
36.39
0.041
0.163
FOO
4
3,303
0.120
0.044
0.127
0.029
0.082
0.173
62.11
0.091
0.164
PRT
4
3,303
0.048
0.047
0.052
0.035
0.001
0.102
54.09
0.007
0.096
Overall
4
2,912
0.008
0.067
0.009
0.064
-0.066
0.084
30.50
-0.073
0.091
FOO
3
2,820
0.057
0.043
0.064
0.032
0.009
0.120
56.41
0.023
0.106
Overall
4
3,190
0.027
0.017
0.031
0.000
0.011
0.051
100.00
0.011
0.051
FOO
6
3,491
0.031
0.066
0.037
0.061
-0.026
0.100
39.54
-0.042
0.115
PRT
4
3,190
0.028
0.047
0.034
0.039
-0.023
0.092
56.09
-0.015
0.084
Overall
5
3,881
0.185
0.061
0.218
0.058
0.154
0.281
35.25
0.143
0.292
FOO
7
4,171
0.210
0.063
0.240
0.055
0.187
0.293
41.27
0.170
0.311
PRT
4
3,713
0.095
0.063
0.112
0.064
0.038
0.186
26.89
0.030
0.195
OFTP
K
N
rbar
SD rbar
ρ
SD ρ
CIL
CIU
%Var
CVL
CVU
Overall
6
3,753
0.251
0.034
0.281
0.000
0.251
0.312
100.00
0.251
0.312
FOO
8
8,700
0.354
0.052
0.401
0.050
0.360
0.442
30.01
0.337
0.465
PRT
5
3,454
0.132
0.042
0.153
0.021
0.111
0.196
81.66
0.127
0.180
FOO
5
4,617
0.360
0.049
0.412
0.038
0.363
0.460
53.73
0.363
0.460
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Overall
5
4,023
0.207
0.022
0.224
0.000
0.206
0.242
100.00
0.206
0.242
FOO
9
8,115
0.303
0.059
0.336
0.055
0.293
0.378
28.37
0.265
0.407
PRT
5
4,023
0.108
0.031
0.119
0.000
0.089
0.150
100.00
0.089
0.150
Overall
4
3,684
-0.165
0.058
-0.186
0.055
-0.251
-0.122
30.91
-0.256
-0.116
FOO
4
3,791
-0.139
0.057
-0.157
0.054
-0.221
-0.094
31.39
-0.226
-0.088
PRT
3
3,571
-0.135
0.025
-0.155
0.000
-0.188
-0.123
100.00
-0.188
-0.123
Overall
4
3,165
-0.333
0.074
-0.367
0.074
-0.448
-0.287
18.13
-0.462
-0.272
FOO
4
3,165
-0.253
0.065
-0.284
0.062
-0.355
-0.213
26.83
-0.363
-0.204
PRT
4
3,165
-0.356
0.076
-0.399
0.078
-0.482
-0.315
17.04
-0.498
-0.299
Overall
5
3,147
0.138
0.063
0.155
0.055
0.093
0.217
39.23
0.084
0.225
FOO
7
5,024
0.180
0.091
0.202
0.093
0.126
0.277
16.33
0.083
0.321
PRT
4
2,979
0.084
0.051
0.094
0.040
0.038
0.149
50.82
0.043
0.145
Overall
3
2,607
0.181
0.053
0.200
0.047
0.133
0.267
37.96
0.140
0.260
FOO
3
908
0.318
0.185
0.368
0.205
0.125
0.610
8.07
0.105
0.631
PRT
3
1,238
0.319
0.046
0.385
0.000
0.322
0.448
100.00
0.322
0.448
OFTP
K
N
rbar
SD rbar
ρ
SD ρ
CIL
CIU
%Var
CVL
CVU
Overall
3
2,867
0.090
0.031
0.105
0.000
0.064
0.146
100.00
0.064
0.146
FOO
5
3,271
0.104
0.047
0.121
0.029
0.074
0.169
71.14
0.084
0.159
PRT
3
2,867
0.041
0.041
0.048
0.030
-0.006
0.103
61.70
0.010
0.087
Overall
7
4,086
0.181
0.052
0.202
0.036
0.157
0.246
61.00
0.155
0.248
FOO
7
4,121
0.240
0.055
0.281
0.043
0.233
0.329
56.17
0.226
0.336
PRT
6
3,795
0.107
0.025
0.126
0.000
0.102
0.150
100.00
0.102
0.150
OFTP
K
N
rbar
SD rbar
ρ
SD ρ
CIL
CIU
%Var
CVL
CVU
Overall
4
3,936
0.122
0.109
0.150
0.127
0.019
0.280
8.93
-0.013
0.313
FOO
4
3,667
0.158
0.148
0.185
0.169
0.015
0.355
4.78
-0.032
0.401
PRT
3
3,534
0.076
0.035
0.089
0.023
0.043
0.136
68.71
0.060
0.119
Note. Overall = overall OFTP, FOO = focus on opportunities, PRT = perceived remaining time. K = cumulative number of studies; N
= cumulative sample size; rbar = sample-size weighted meta-analytic correlation; SD rbar = standard deviation of rbar; ρ = sample size-
weighted and reliability-corrected meta-analytic correlation; SDρ = standard deviation of ρ; CI = 95% confidence interval for ρ; %var
= variance attributable to statistical artifacts (sampling error & unreliability); CV = 80% credibility interval for ρ.
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Table 3. Results of OFTP Dimension Meta-Analysis (Focus on Opportunities & Perceived Remaining Time)
OFTP
K
N
rbar
SD rbar
ρ
SD ρ
CIL
CIU
%Var
CVL
CVU
FOO - PRT
16
7,549
0.617
0.109
0.718
0.119
0.656
0.781
13.186
0.566
0.870
Note. FOO = focus on opportunities, PRT = perceived remaining time. K = cumulative number of studies; N = cumulative sample size;
rbar = sample-size weighted meta-analytic correlation; SD rbar = standard deviation of rbar; ρ = sample size-weighted and reliability-
corrected meta-analytic correlation; SDρ = standard deviation of ρ; CI = 95% confidence interval for ρ; %var = variance attributable to
statistical artifacts (sampling error & unreliability); CV = 80% credibility interval for ρ.
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Table 4. Meta-Analytic Correlation Table & Sources of Meta-Analytic Correlations
Age
OFTP
SOC
Emotional Exhaustion
Job Satisfaction
Task Performance
Work Engagement
Age
1.00
Current - Main
Analysis
N = 9,613
K = 22
Moghimi et al.
(2017)
N = 9,613
K = 10
Brewer et al.
(2004)
N = 10,818
K = 35
Ng et al.
(2010)
N = 151,105
K = 388
Ng et al.
(2008)
N = 17,807
K = 52
Current - Ad Hoc
Analysis
N = 26,751
K = 31
OFTP
rxy = -0.52
1.00
Current - Main
Analysis
N = 3,936
K = 4
Current - Main
Analysis
N = 3,684
K = 4
Current - Main
Analysis
N = 3,753
K = 6
Current - Main
Analysis
N = 2,867
K = 3
Current - Main
Analysis
N = 4,023
K = 5
SOC
rxy = 0.04
rxy = 0.12
1.00
Moghimi et al.
(2017)
N = 3,719
K = 9
Moghimi et al.
(2017)
N = 4,001
K = 11
Moghimi et al.
(2017)
N = 3,110
K = 10
Moghimi et al.
(2017)
N = 5,385
K = 11
Emotional Exhaustion
rxy = -0.16
rxy = -0.17
rxy = 0.01
1.00
Lee et al.
(1996)
N = 4,000
K = 17
Swider et al.
(2010)
N = 4,602
K = 14
Crawford et al
(2010)
N = 25,998
K = 54
Job Satisfaction
rxy = 0.18
rxy = 0.25
rxy = 0.21
rxy = -0.26
1.00
Iaffaldano et al.
(1985)
N = 12,192
K = 217
Christian et al.
(2011)
N = 9,725
K = 20
Task Performance
rxy = 0.06
rxy = 0.09
rxy = 0.19
rxy = -0.13
rxy = 0.15
1.00
Christian et al.
(2011)
N = 4,562
K = 14
Work Engagement
rxy = 0.12
rxy = 0.21
rxy = 0.34
rxy = -0.39
rxy = 0.46
rxy = 0.36
1.00
Note. K = cumulative number of studies; N = sample size; rbar = sample-size weighted meta-analytic correlation. Sources of meta-
analytic correlations appear above diagonal.
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Table 5. Tests of Incremental Effects of OFTP Above and Beyond Age and SOC
Model 1 R2
Model 2 R2
ΔR2
ΔR2%
Fpartial
p
Emotional Exhaustion
0.026
0.118
0.092
9.180%
569.133
<.001
Job Satisfaction
0.074
0.214
0.140
13.989%
1081.476
<.001
Task Performance
0.039
0.052
0.013
1.265%
67.255
<.001
Work Engagement
0.123
0.195
0.071
7.136%
568.311
<.001
Note. Model 1 = Age + SOC; Model 2 = Age + SOC + OFTP. R2 = variance explained. ΔR2 = change in R2 from Model 1 to Model 2;
ΔR2% = change in R2 from Model 1 to Model 2 expressed as a percentage; Fpartial = inferential test of ΔR2/ ΔR2%; p = observed
probability of Fpartial. For any given outcome, a statistically significant Fpartial (p < .05) suggests that OFTP incrementally predicts
variance above and beyond the influence of age and SOC.
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Table 6. Results of Relative Weights Analysis
Emotional Exhaustion
Predictor
B
SEB
t-value
p
RW
%R2
R2 = .118
Age
-0.349
0.015
-23.336
<0.001
0.056
47.883
F = 243.040, p < .001
SOC
0.065
0.013
5.078
<0.001
0.001
1.117
OFTP
-0.360
0.015
-23.854
<0.001
0.060
51.000
Job Satisfaction
Predictor
B
SEB
t-value
p
RW
%R2
R2 = .214
Age
0.405
0.013
30.233
<0.001
0.077
36.086
F = 550.407, p < .001
SOC
0.141
0.012
12.249
<0.001
0.033
15.574
OFTP
0.444
0.013
32.883
<0.001
0.103
48.340
Task Performance
Predictor
B
SEB
t-value
p
RW
%R2
R2 = .052
Age
0.123
0.016
7.602
<0.001
0.008
15.098
F = 91.969, p < .001
SOC
0.170
0.014
12.239
<0.001
0.033
63.635
OFTP
0.133
0.016
8.200
<0.001
0.011
21.267
Work Engagement
Predictor
B
SEB
t-value
p
RW
%R2
R2 = .195
Age
0.272
0.013
20.583
<0.001
0.036
18.438
F = 516.726, p < .001
SOC
0.317
0.013
23.837
<0.001
0.060
30.925
OFTP
0.287
0.011
25.213
<0.001
0.099
50.637
Note. R2 = variance explained; F = omnibus test of model significance. B = regression weight; SEB = standard error for B; RW = raw
relative weight; %R2 = rescaled raw relative weight as a percent of total variance explained by model.
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Table 7. Results of Path Analysis
A-Paths
Predictor
Outcome
B
SEB
z-value
p
95% CI Lower
95% CI Upper
Age
OFTP (R2 =.270)
-0.520
0.011
-45.695
<0.001
-0.542
-0.498
SOC (R2 =.001)
0.038
0.013
2.854
0.004
0.012
0.064
B-Paths
Predictor
Outcome
B
SEB
z-value
p
95% CI Lower
95% CI Upper
OFTP
Emotional Exhaustion (R2 = .123)
-0.360
0.015
-24.542
<0.001
-0.388
-0.331
SOC
0.065
0.013
5.224
<0.001
0.041
0.090
Age
-0.349
0.015
-23.840
<0.001
-0.378
-0.321
OFTP
Job Satisfaction (R2 =.200)
0.444
0.014
32.092
<0.001
0.417
0.471
SOC
0.141
0.012
11.954
<0.001
0.118
0.165
Age
0.405
0.014
29.298
<0.001
0.378
0.433
OFTP
Task Performance (R2 =.046)
0.133
0.015
8.790
<0.001
0.104
0.163
SOC
0.170
0.013
13.119
<0.001
0.145
0.196
Age
0.123
0.015
8.091
<0.001
0.093
0.153
OFTP
Work Engagement (R2 =.174)
0.317
0.014
22.650
<0.001
0.290
0.344
SOC
0.287
0.012
23.956
<0.001
0.263
0.310
Age
0.272
0.014
19.420
<0.001
0.245
0.299
Note. R2 = variance explained; B = regression weight; SEB = standard error for B; 95% CI = 95% confidence interval for B.
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Table 8. Summary of Indirect Effects
Summary of Indirect Effect (IE)
IE
SEIE
z-value
p
95% CI Lower
95% CI Upper
Age to OFTP to Emotional Exhaustion
0.187
0.009
21.621
<0.001
0.170
0.204
Age to OFTP to Job Satisfaction
-0.231
0.009
-26.262
<0.001
-0.248
-0.214
Age to OFTP to Task Performance
-0.069
0.008
-8.632
<0.001
-0.085
-0.054
Age to OFTP to Work Engagement
-0.165
0.008
-20.294
<0.001
-0.181
-0.149
Age to SOC to Emotional Exhaustion
0.002
0.001
2.505
0.012
0.001
0.004
Age to SOC to Job Satisfaction
0.005
0.002
2.776
0.005
0.002
0.009
Age to SOC to Task Performance
0.006
0.002
2.789
0.005
0.002
0.011
Age to SOC to Work Engagement
0.011
0.004
2.834
0.005
0.003
0.018
Note. IE = indirect effect (i.e., product term of corresponding “A” and “B” path coefficients from Table 7); SEIE = standard error for
IE; 95% CI = Monte Carlo confidence intervals for IE.
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Figure 1. Conceptual Model and Nomological Network of Assumed Antecedents and Outcomes of Occupational Future Time
Perspective
Note. Within parentheses, (+) indicates generally positive relationships with OFTP noted in literature, (–) indicates generally negative
relationships with OFTP noted in the literature, (=) indicates generally equivocal relationships with OFTP noted in the literature.
Double-headed arrows indicate that these relationships are assumed to be correlational, not causal, in nature.
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Figure 2. Outline of the Literature Search Process
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Appendix: Included Studies & Coded Constructs
Study
Age
Job Tenure
Organizational Tenure
Education
Self-Rated Physical Health
Gender
Work Hours
Job Demands
Job Complexity
Job Autonomy
Job Satisfaction
Org. Commitment
Work Engagement
Emotional Exhaustion
Retirement Intentions
Continuance Intentions
Achievement Motivation
Learning Motivation
Task Performance
Contextual Performance
SOC Strategy Use
Bal et al. (2015)
Barbieri et al. (2015)
Betts (2013)
Chen (2015)
de Lange (2016)
Ebbert (2014)
Fok (2011)
Froehlich et al. (2016)
Gielnik et al. (2012)
Gielnik et al. (2016)
Grube (2009)
Ho & Yeung (2016)
Kochoian, Raemdonck, Coertjens, et al. (2017)
Kochoian, Raemdonck, Frenay, & Zacher (2017)
Kooij & Zacher (2016), Study 1
Kooij & Zacher (2016), Study 2
Lopina (2015)
McCausland (2014)
Nijendijk (2010)
Profili et al. (2017)
Schmitt, Gielnik, et al. (2013)
Schmitt, Zacher, & de Lange (2013), Study 1
Schmitt, Zacher, & de Lange (2013), Study 2
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Stynen (2013)
TC (Mauritz, 2012; van der Maarel, 2011)
van Solinge (2014)
Weikamp (2015)
Weikamp & Göritz (2015)
Weikamp & Göritz (2016)
Zacher (2013)
Zacher (2016) [Study 1]
Zacher (2016) [Study 2]
Zacher (2016) [Study 3]
Zacher (2016) [Study 4]
Zacher (2016) [Study 5]
Zacher (2016) [Study 6]
Zacher & Frese (2009)
Zacher & Frese (2011)
Zacher et al. (2010)
Zacher & Yang (2016)
Note. ” indicates presence of relevant OFTP (i.e., overall OFTP, focus on opportunities, and/or perceived time remaining) effect
size(s) for a given antecedent/outcome. SOC = selection, optimization, compensation strategy use; TC = thesis circle.

Supplementary resource (1)

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... A systematic review of quantitative studies (Henry et al., 2017) and a metaanalysis (Rudolph et al., 2018) showed that occupational future time perspective is strongly negatively related to worker age, and weakly or moderately related to other individual characteristics (e.g., education, subjective health) and contextual variables (e.g., job autonomy). Moreover, these reviews also showed that occupational future time perspective is positively related to important work and career outcomes, including job satisfaction, organizational commitment, work engagement, motivation to continue working beyond traditional retirement age, and job performance, as well as negatively related to retirement intentions. ...
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