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Work, Aging and Retirement, 2015, Vol. 1, No. 4, pp. 369–381doi:10.1093/workar/wav002Advance Access publication March 14, 2015Article

Correspondence concerning this article should be addressed to Julia G. Weikamp, University of Freiburg, Department of Occupational and Consumer Psychology, Engelbergerstraße 41, 79085 Freiburg, Germany. E-mail: [email protected] Editor: Donald Truxillo, PhD

How Stable Is Occupational Future Time Perspective Over Time? A Six-Wave Study Across 4 Years

Julia G. Weikamp and Anja S. Göritz

Department of Occupational and Consumer Psychology, University of Freiburg, Freiburg, Germany

A B S T R A C TThe occupational future time perspective (OFTP) reflects people’s perceptions of their remaining time and opportu-nities in their future working lives. The aim of this study was 2-fold. First, we determined the extent to which people’s OFTP decreased over time. Second, we explored whether the rate of decrease in OFTP depended on age. We con-ducted a longitudinal study and measured OFTP (using the subscales of remaining time and remaining opportuni-ties) 6 times over 4 years. Multilevel analyses revealed that people perceived losses of remaining occupational time and remaining occupational opportunities over time. We did not observe that the decrease in perceived remaining opportunities depended on age. However, age moderated the decrease in perceived remaining time over time such that younger people felt that their remaining time decreased faster than did older people. The theoretical and practical implications of the stability of OFTP over time are discussed.

People’s perceptions of time are long-studied phenomena because they affect human behavior in many areas. As long ago as 1939, Lewin (1939) proposed that the manner in which humans think about their future time affects their present behaviors. Furthermore, he noted that this future time perspective changes with age. Among the many differ-ent definitions of future time perspective (FTP; for an overview, Seijts, 1998), we refer to the definition of Lang and Carstensen (2002): FTP comprises the time horizon of the whole life by capturing the expec-tations and beliefs that people have about the length of their future time and opportunities therein [Carstensen, 2006; Cate & John, 2007; Kooij, Bal, & Kanfer, 2014, in contrast to Carstensen (2006), Cate and John (2007) conceptualize future time perspective in terms of two dis-tinct foci: focus on limitations (i.e., the perception of limited time and limited opportunities), and focus on opportunities (i.e., the perception of infinite time and many opportunities).].

When people age, they differ in their perception of FTP (Carstensen, 2006) such that FTP dwindles over time from open-ended to constrained (Allemand, Hill, Ghaemmaghami, & Martin, 2012; Bal, de Lange, Zacher, & Van der Heijden, 2013; Carstensen, 2006; Carstensen, Isaacowitz, & Charles, 1999; Cate & John, 2007; Kooij, de Lange, Jansen, & Dikkers, 2013; Kooij et al., 2014; Kooij & Van de Voorde, 2011; Lang & Carstensen, 2002; Seijts, 1998; Zacher & Frese, 2009). Having an open-ended FTP is associated with many pos-itive outcomes; for example, people who perceive themselves as having an open-ended FTP are more conscientious (Cate & John, 2007), are more highly educated (e.g., Bal et al., 2013), have a higher promotion focus (Kooij et al., 2014), exhibit more gratitude (Allemand & Hill, in

press), report greater psychological well-being (Allemand et al., 2012; Drake, Duncan, Sutherland, Abernethy, & Henry, 2008; Hicks, Trent, Davis, & King, 2012), have better general health statuses (Kooij & Van de Voorde, 2011), are more likely to report to participate in physical activities (Löckenhoff & Carstensen, 2004; Stahl & Patrick, 2011), have larger networks of friends, and are more likely to engage in posi-tive social exchanges (e.g., informational support; Windsor, Fiori, & Crisp, 2011).

As early as 1964, Nuttin (1964) proposed that people’s psychologi-cal future has a substantial effect on their motivations. The perception of time affects goal selection and pursuit because people prioritize knowledge-oriented or instrumental goals when they perceive them-selves as having an open-ended amount of time left, whereas people prioritize emotion-oriented goals when they perceive themselves as having a constrained amount of time left (Carstensen et al., 1999; Lang & Carstensen, 2002; Zacher, Degner, Seevaldt, Frese, & Lüdde, 2009). These findings resonate with knowledge about customer satisfaction and the importance of different work or job characteristics. Customers with an open-ended FTP appreciate excellent and high-quality ser-vices, whereas customers with a constrained FTP appreciate services that give them positive emotions (Kuppelwieser & Sarstedt, 2014). Further, people who perceive themselves as having an open-ended FTP put emphasis on work or job characteristics that can fulfill their striving for growth, development, status, recognition, power, and pres-tige (Bal, Jansen, van der Velde, de Lange, & Rousseau, 2010; Kooij et al., 2013, 2014; Kooij & Van de Voorde, 2011). In contrast, people who perceive themselves as having a constrained FTP put emphasis on

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370 • Weikamp & Göritz

work or job characteristics that can fulfill their striving for generativ-ity (Bal et al., 2010; Kooij, de Lange, Jansen, Kanfer, & Dikkers, 2011; Kooij & Van de Voorde, 2011).

Focusing on Working Life: Occupational Future Time PerspectiveTruxillo and Fraccaroli (2013) underlined that FTP is an interest-ing construct in work and organizational psychology because it may shed light on age-related outcomes at the workplace. However, as the focus of work and organizational psychology does not lie on the entire life span but rather on the working life, it may be useful to explore a FTP in reference to one’s working life. In line with this rea-soning, Zacher and Frese (2009) developed the Occupational Future Time Perspective (OFTP) scale. The basis of OFTP is the FTP scale previously described, but the items of the FTP scale are qualified by the word “occupational.” Thus, OFTP captures people’s perceptions of their future in the work context (Zacher & Frese, 2009). The con-cept of OFTP differentiates between two dimensions: remaining time and remaining opportunities at work. Remaining time at work captures how much time people perceive themselves as having left in their future working lives before they exit the labor force (Carstensen, 2006; Zacher & Frese, 2009). People with an open-ended remaining time perceive no time limits, restrictions or boundaries in their occu-pational future. Remaining opportunities at work are a contextualized form of optimism and capture people’s beliefs about the quantity of possible work-related opportunities, options, plans, and goals that they can pursue in their occupational future (Cate & John, 2007; Zacher & Frese, 2009, 2011). People who perceive themselves as having many remaining opportunities conceive their occupational future as being full of new opportunities, options, plans, and goals.

Although research has increasingly dealt with FTP in the last years, “research on FTP is still at an early stage of development” (Kuppelwieser & Sarstedt, 2014, p. 2625). This statement applies even more so to the concept of OFTP. To our knowledge, the study at hand is the first to empirically address the stabilities of both of the dimen-sions of OFTP with a longitudinal study design. Up to date, OFTP has only been correlated with age in cross-sectional studies, and there exists only one two-wave study across 2 years on the stability of per-ceived remaining opportunities over time (Schmitt, Gielnik, Zacher, & Klemann, 2013). Therefore, the aim of our study was twofold. First, we quantified the extent to which people’s OFTP decreased over time. Second, we explored whether the rate of the decrease in OFTP depended on a person’s age. Thereby, we contribute to a better under-standing of how people perceive their occupational future time across their lifespan. By identifying the rate at which OFTP changes on the continuum from open-ended to constrained, we offer insights into the psychological aspects of aging at work because studying the psycholog-ical variable of time perspective helps reveal the underlying processes (Bal et al., 2010, 2013). Further, we can derive practical implications for human resource management from a lifespan perspective.

Since the introduction of the construct of OFTP in 2009, research has explored the following two areas: (a) antecedents and consequences of OFTP, and (b) the effects of age on OFTP. Regarding the anteced-ents of OFTP, good mental health, having high job control, and having a complex job all help people maintain a perception of many remaining opportunities (Gielnik, Zacher, & Frese, 2012; Zacher & Frese, 2009,

2011). Further, the work characteristics of job complexity and job con-trol buffer the negative effect of the employees’ ages on their perceptions of remaining opportunities (Zacher & Frese, 2009, 2011). Regarding the consequences of perceiving one’s OFTP as open-ended or constrained, people exhibit greater work engagement over time if they perceive them-selves as having many remaining opportunities (regardless of whether they have high or low job control or are optimistic in general). However, if employees perceive themselves as having few remaining opportunities at work, it is important to provide these employees with high job control to foster their work engagement (Schmitt, Gielnik, et al., 2013; Schmitt, Zacher, & de Lange, 2013). Moreover, people exhibit more organiza-tional citizenship behavior that is directed toward the organization and are more satisfied with their jobs if they perceive themselves as having many remaining opportunities left (Weikamp & Göritz, 2014).

Additionally, OFTP links age and work-related outcomes through mediation and acts as a moderator in the relationship between work characteristics and employees’ work behaviors. Referring to the role of OFTP as a mediator, having a complex job fosters employees’ focus on remaining opportunities, which, in turn, heightens work performance. Furthermore, people’s focus on remaining opportunities mediated the relationship between age and work performance (Zacher, Heusner, Schmitz, Zwierzanska, & Frese, 2010). In a study of 84 small business owners in Germany, the perception of many remaining opportunities was found to mediate the relationship between the business owners’ ages and venture growth (Gielnik et al., 2012). Hence, business own-ers ensure entrepreneurial success despite growing old if they maintain a perception of having many remaining opportunities. Referring to the role of OFTP as a moderator, older job seekers engage in more inten-sive job search activities if they either have a proactive personality or perceive themselves as having open-ended amount of remaining time. Accordingly, open-ended remaining time compensates for a low proac-tive personality in the context of job search behavior (Zacher, 2013). Overall, the psychological variable of OFTP affects many workplace behaviors and might be superior to chronological age in terms of explain-ing “how aging affects the experience of work” (Schwall, 2012, p. 181).

One variable that has been often associated with OFTP is chrono-logical age. Zacher and colleagues conducted several studies and found that perceived remaining time and perceived remaining opportunities were both negatively related to age in samples of full-time employ-ees in Germany and Switzerland. Thereby, the correlation between employee age and perceived remaining time is stronger than the cor-relation between employee age and perceived remaining opportunities (Zacher et al., 2010; Zacher & Frese, 2009, 2011). Notably, the cor-relation between employee age and perceived remaining opportunities was found to only be weak in a sample of business owners (Gielnik et al., 2012) and in a sample of older job seekers (Zacher, 2013). Among these job seekers, the relationship between employee age and perceived remaining time was also found to only be weak. Overall, the cross-sectional studies of the relationship between age and OFTP indi-cate that greater age is associated with the perceptions of a constrained rather than an open-ended remaining time and fewer rather than more remaining opportunities at work.

Stability of the OFTPThe findings regarding the negative relationship between OFTP and age imply that OFTP is not stable over time and is thus an age-related


Stability of Occupational Future Time Perspective • 371

construct. Accordingly, FTP has been conceptualized as changing across the lifetime from open-ended to constrained (Seijts, 1998; Zacher & Frese, 2009; Zacher et al., 2009). As OFTP is based on FTP, OFTP should also dwindle across the lifespan. However, the only hints for negative relationships of both dimensions of OFTP with age have so far come only from cross-sectional research (Zacher, 2013; Zacher & Frese, 2009). Moreover, we can primarily refer to data about the rela-tionship between perceived remaining opportunities and age because the majority of the studies, which have dealt with OFTP, accounted for the perception of remaining opportunities but not remaining time. Currently, we lack information from longitudinal research regarding how fast people’s OFTP change across the continuum from open-ended to constrained over time and whether this change differs for people who differ in chronological age (Schalk, Van der Heijden, de Lange, & Van Veldhoven, 2011).

H Y P O T H E S E S A N D R E S E A R C H Q U E S T I O N SBoth the perception of remaining time and the perception of remain-ing opportunities refer to working life from the entrance to the exit of the labor force. As these perceptions have been described as two distinct, bipolar dimensions of OFTP and as the correlation between age and perceived remaining opportunities has been found to be weaker (r = −.62) than the correlation between age and perceived remaining time (r = −.82; Zacher & Frese, 2009), we examined the effects of age and time on each dimension separately. The perception of remaining time ranges from open-ended to constrained. People with open-ended remaining time perceptions are characterized as perceiving no time limits, restrictions, or boundaries in their occupa-tional future. Typically, young people perceive themselves as having much occupational time left, and they objectively have more time left than older people. In cross-sectional studies, these deliberations have received empirical support as age and perceived remaining time have been found to be negatively associated (Zacher, 2013; Zacher & Frese, 2009, 2011).

Additionally, young and old people might differ in the decline in perceived remaining time over time, and we further explored this issue. The effect of time and thus increasing age on the decline of per-ceived remaining time could occur in either of two different ways. On the one hand, older adults might experience a steeper decline in per-ceived remaining time than younger adults over time. People work for an average of approximately 40–45 years in their life. Younger people who are in their mid-20s have approximately 40 years of working life ahead of them. After a period of 4 years, these people still have 36 years of working life ahead of them; that is, approximately one-tenth of their working life elapses over a period of 4 years, but having 36 years left to work is still a long time frame. In contrast, people who are 60 years old have approximately 5 years of working life ahead. After 4 years, these people have only 1 year of working life ahead; that is, four-fifths of their remaining time in working life elapses over a period of 4 years, and hav-ing 1 year left to work is a very short time frame.

On the other hand, younger adults might experience a steeper decline in their perceived remaining time than older adults. Younger people typically experience more life transitions and thus face more endings of stages of life than older people. Facing endings has been found to be associated with decreased FTP (for an overview, see Carstensen et al., 1999). Settersten and Hagestad (1996) differentiated

between the following five educational and work transitions: (a) fin-ishing school, (b) entering the full-time labor force, (c) settling on a career, (d) reaching the peak of the work career, and (e) entering retire-ment. These authors interviewed 319 people in the United States about their perceptions of age deadlines for these life transitions. The major-ity of interviewees mentioned that people should have passed through four of the five transitions by early or middle adulthood (i.e., enter the full-time labor force in one’s early 20s, finish school at the age of 25, settle on a career at the age of 30, and reach the peak of the work career at the age of 40). In late adulthood, people should have entered retire-ment (i.e., at the age of 65). Similar age deadlines have also been found among Danish people; the age deadlines for working in one’s first job or finishing school, settling on a career, and entering retirement were 26, 27, and 65 years, respectively (Berntsen & Rubin, 2004). According to these results, the majority of educational and work transitions are assumed to occur in early adulthood. Based on cross-sectional studies, we assume that perceived remaining time decreases over time. Hence, older people are expected to perceive themselves as having a more con-strained remaining time than younger people. Moreover, we explore age as a potential moderator of the change in perceived remaining time over time.

Hypothesis 1: Perceived remaining time at work decreases (1) over time and thus (2) decreases with increasing age.

Research Question 1: Is there an interaction effect of time and age on perceived remaining time at work such that people’s perceived remaining time at work changes differently for young versus old people over time?

The perception of remaining opportunities ranges from many to few. People who perceive themselves as having many remaining opportu-nities perceive their occupational future as full of new opportunities, options, plans, and goals. Young people might perceive themselves as having endless occupational possibilities; for example, from choosing an occupation or an employer to moving up the career ladder. Further, the probability of finding a new job is higher for younger than older adults (Finkelstein, Burke, & Raju, 1995). This reasoning corresponds to cross-sectional findings about the negative relationship between age and per-ceived remaining opportunities (Zacher & Frese, 2009). Therefore, we expect perceived remaining opportunities to decrease over time. If this expectation holds, older people are expected to perceive fewer remain-ing opportunities than younger people. Moreover, we explore whether age moderates people’s perception of remaining opportunities as time progresses.

Hypothesis 2: Perceived remaining opportunities at work decrease (1) over time and thus (2) decrease with increasing age.

Research Question 2: Is there an interaction effect of time and age on perceived remaining opportunities such that people’s perceived remaining opportunities at work change differently for young versus old people over time?



M E T H O DData Collection and ProcedureThe data were obtained via a university-based online panel that con-sisted of people from all walks of (occupational) life (Göritz, 2014). We analyzed six measurement points of OFTP across 4 years. The measurements took place in (a) November 2009, (b) October 2010, (c) May 2011, (d) June 2011, (e) February 2013, and (f) July 2013. We obtained data from 3,168 German-speaking panelists with at least two measurements of OFTP. To ensure that these measurements were at least 5 months apart, we excluded 176 participants who only com-pleted Measurement Points 3 and 4, which were less than 5 months apart. As we were studying time perspective in working life, we excluded all retired (602 participants) and unemployed people (141 participants). For the same reason, we excluded people under the age of 18 years old at Measurement Point 1 (16 participants) and those who were older than 65 years at Measurement Point 6 (53 partici-pants), which resulted in a final N = 2,180 [We chose the thresholds of 18 and 65 years based on the typical working age in Germany. Concerning the low age threshold, people below 15 years of age are prohibited from working in Germany ( Jugendarbeitsschutzgesetz ( JArbSchuG) §5 part 1), and they are typically in (vocational) educa-tion (Wingerter, 2011). Concerning the high age threshold, most work contracts and labor contracts refer to legal retirement age (Zeit Online, 2011). Although the German government has discussed and began to gradually increase the legal retirement age, people who were 65 years old at the last measurement point in our study were not affected by the raising of the legal retirement age. Their legal retirement age was 65 years (Sechstes Buch Sozialgesetzbuch § 235, Part 2).]. Of the final sample, 718 people completed two waves, 570 people completed three waves, 457 people completed four waves, 278 people completed five waves, and 157 people completed six waves.

SampleOn average, the participants were 39 years old (SD = 11), and the ages ranged from 18 to 61 years at Measurement Point 1 [As the participants were recruited via a panel, we had access to each partici-pant’s master data (e.g., year of birth) and were thus able to describe the sample based on all participants (i.e., N = 2,180). We compared the panelists who completed two waves (the random sample of peo-ple who completed two waves; n = 157) with those who completed six waves (n = 157). We found no difference between these groups in terms of education (for two waves, M = 3.92; SD = 0.91; for six waves, M = 3.89; SD = 1.07; t(304.30) = 0.34; p = .73). However, the partici-pants who completed six waves were significantly older than the par-ticipants who completed only two waves (for two waves, M = 36.19; SD = 10.90; for six waves, M = 44.24; SD = 11.16; t(312) = −6.64, p < .001). Additionally, the proportion of female participants was higher among the participants who completed two rather than six waves (for two waves, M = 0.65; SD = 0.48; for six waves, M = 0.54; SD = 0.50; t(311.39) = 2.08; p = .039).]. Among the participants, 62.6 % were women. Regarding education levels, 2.5 % of the participants held a doctorate, 32.3 % held a master’s degree, 29.7 % held a high-school diploma, 26.9 % held a tenth-grade degree, 8.2 % held a ninth-grade degree, 0.2 % had no degree (yet), and 0.2 % did not indicate their educational degree. The majority of the participants (82.3 %) were employed when they signed-up for the panel. Smaller proportions

were in school or studying (12.0 %), on parental leave (1.3 %), or did not categorize themselves into one of these categories (4.4 %).

MeasuresOccupational Future Time Perspective To assess individual changes over time, we applied the same scale to measure OFTP across the six measurement points (Hox, 2010). We used the German version of the OFTP scale, which was developed with a German sample (Zacher & Frese, 2009). The scale consists of two subscales. The subscale “remaining time” captures how much time people perceive themselves as having left in their future working life (sample item: “Most of my occupational life lies ahead of me”). The subscale “remaining opportunities” measures how many opportuni-ties people perceive themselves as having left in their future working life (sample item: “Many opportunities await me in my occupational future”). Both scales consist of three items and were answered on a Likert scale from 1 (does not apply at all) to 5 (applies completely). Regarding “remaining time,” the internal consistencies ranged from α = .64 to .74 across the six measurement points, and the internal consistencies of “remaining opportunities” ranged from α = .92 to .95 across the six measurement points (Table 1).

Control Variables We controlled for two demographic variables. First, we controlled for gender because women might perceive themselves as having fewer opportunities at work compared to men (see the glass ceiling effect; Morrison, White, & Van Velsor, 1987). Further, women are more likely to interrupt or slow down their career due to parental duties and thus might perceive themselves as having less time in working life in general. Second, we controlled for education (1 = no degree (yet), 2 = ninth-grade degree, 3 = tenth-grade degree, 4 = high-school diploma, 5 = master’s degree, and 6 = doctorate) because people with a higher educational degrees can choose between more occupational options. Consequently, these people might perceive themselves as having more occupational opportunities. Indeed, Zacher and Frese (2009) found a positive relationship between people’s education and their perceived remaining opportunities at work.

Analytic ApproachFirst, we conducted principal axis analyses to test the factor structure of OFTP. As the perceptions of remaining time and remaining oppor-tunities are correlated (e.g., Zacher & Frese, 2009), we used oblique rotation. The two-factor structure of OFTP found by Zacher and Frese (2009) was confirmed at each measurement point. Consequently, we performed separate analyses for each subscale. Next, we calculated the test-retest reliabilities of remaining time and remaining opportunities between each measurement point and all other measurement points with Fisher’s z transformations.

As the measurement points (Level 1) were nested within the par-ticipants (Level 2), we conducted multilevel analyses. First, we defined the basic models (i.e., an unconditional means model, a random inter-cept model, and a random intercept and random slope model). For remaining time, the random intercept model fit the data best; that is, people only differed in their perceptions of remaining time at the first measurement point, but their developments of perceived remain-ing time over time were the same. For remaining opportunities, the



Table 1. M

eans (M

), Stand

ard Deviatio

ns (S

D), Reliabilities, and Intercorrelatio

ns of the Study Variables

Varia

ble

nm

sdco

rrel

atio

ns

Con

trol

12

34

56

78

910

1112

1314

15

1.G

ende

r2,

180

0.63

0.48

—2.

Educ

atio

n2,

176

3.93

1.02

−.05

*—

3.A

ge [y

ears

]2,

180

39.1

411

.41

−.12

***

−.13

***

—R

emai

ning

tim

e4.

t11,

332

2.92

1.05

.09*

**.1

8***

−.73

***

(.74)

5.t2

1,28

62.

830.

99.0

9***

.13*

**−.

71**

*.7

8***

(.70)

6.t3

1,29

02.

881.

03.0

7*.1

9***

−.74

***

.75*

**.7

5***

(.74)

7.t4

1,51

12.

841.

02.0

8**

.18*

**−.

68**

*.7

6***

.78*

**.7

8***

(.73)

8.t5

1,13

32.

860.

97.1

2***

.11*

**−.

74**

*.7

4***

.72*

**.7

9***

.72*

**(.6

6)9.

t690

02.

680.

97.0

7*.1

1**

−.68

***

.67*

**.6

8***

.73*

**.7

2***

.75*

**(.6

4)R

emai

ning

opp

ortu

nitie

s10

.t1

1,33

23.

111.

15.0

1.2

2***

−.45

***

.65*

**.5

1***

.50*

**.5

3***

.48*

**.4

2***

(.93)

11.

t21,

286

2.97

1.11

−.02

.17*

**−.

29**

*.5

0***

.53*

**.4

3***

.45*

**.4

2***

.37*

**.7

0***

(.93)

12.

t31,

290

3.04

1.08

−.05

.25*

**−.

47**

*.5

4***

.51*

**.6

3***

.56*

**.5

5***

.55*

**.6

4***

.67*

**(.9

5)13

.t4

1,51

13.

091.

13.0

4.2

0***

−.45

***

.55*

**.5

1***

.56*

**.6

0***

.55*

**.5

6***

.67*

**.6

8***

.74*

**(.9

4)14

.t5

1,13

33.

111.

08.0

5.1

8***

−.58

***

.60*

**.5

8***

.64*

**.6

0***

.68*

**.6

2***

.64*

**.5

8***

.71*

**.6

7***

(.92)

15.

t690

02.

911.

16.0

3.2

1***

−.40

***

.49*

**.4

2***

.47*

**.5

1***

.50*

**.6

2***

.56*

**.5

4***

.62*

**.6

3***

.66*

**(.9

4)

Not

e. A

ge: m

easu

red

at t1

. Gen

der:

0 =

man

; 1 =

wom

an. E

duca

tion:

1 =

no

degr

ee (y

et);

2 =

nin

th-g

rade

deg

ree;

3 =

tent

h-gr

ade d

egre

e; 4

= h

igh-

scho

ol d

iplo

ma;

5 =

mas

ter’s

deg

ree;

6 =

doc

tora

te. t

1 =

Mea

sure

men

t Poi

nt 1

; t2

= M

easu

rem

ent P

oint

2, a

nd so

fort

h. Th

e int

erna

l con

siste

ncie

s are

pre

sent

ed in

par

enth

eses

on

the d

iago

nal.

*p ≤

.05,

**p

≤ .0

1, **

*p ≤

.001

.



random intercept and random slope model fit the data best; that is, people differed in their perceptions of remaining opportunities at the first measurement point and in their development of perceived remain-ing opportunities over time (Table 2).

After defining the basic models, we followed Hox (2010) and grand-mean-centered the age variable. We recoded the first measure-ment point as 0 so that we could interpret the intercepts as the base levels of remaining time and remaining opportunities. To indicate the months that had passed since the first measurement point, we recoded Measurement Point 2 as 11 because the second measure-ment point occurred 11 months after the first measurement point. Similarly, we recoded Measurement Points 3, 4, 5, and 6 as 18, 19, 39, and 44, respectively. In Model 1 (the null model), we only included the intercept without any explanatory variables. In Model 2, we addi-tionally included the time variable (i.e., the measurement points) and age as a linear predictor. In Model 3, we additionally included the two higher-level, time-invariant variables of gender and education. In Model 4, we added the interaction term of Time × Age.

R E S U LT SDescriptive Statistics and IntercorrelationsTable 1 shows the means, standard deviations, reliabilities, and cor-relations of the study variables. The descriptive statistics for remain-ing time and remaining opportunities were above the midpoint at all measurement points. The subscales of OFTP, that is, remaining time and remaining opportunities, were strongly correlated. Consistent with previous findings, older people exhibited shorter remaining time and fewer remaining opportunities. Gender was not related to remaining opportunities, but women perceived themselves as having a slightly more open-ended remaining time. Higher levels of education were associated with longer perceived remaining time and more per-ceived remaining opportunities, albeit these correlations were weak. Concerning the test-retest reliabilities, we found mean test-retest reli-abilities of r = .74 for remaining time and r = .65 for remaining oppor-tunities. Therefore, the perception of remaining time was more stable over time than the perception of remaining opportunities (z = 3.35; p < .001).

Tests of the Hypotheses and Research QuestionsTo test Hypothesis 1, we calculated multilevel analyses with a random intercept model as the base model [Table 3; the intraclass correlation

(ICC) was calculated according to the equation ρ = σ2intercept/(σ2

intercept + σ2

e). The explained Level 1-variance was calculated according to the equation (σ2

e Model 1 − σ2e Model X)/σ2

e Model 1, and the explained Level 2-vari-ance was calculated according to the equation (σ2

intercept Model 1 − σ2intercept

Model X)/σ2intercept Model 1 (Model X can be Model 2, Model 3, or Model

4). All of these equations were based on Hox (2010). The explained Level 1-variance is the amount of explained variance at the first level (here: the measurement points) by the predictor variables that are additionally included in Model X but are not included in Model 1. The explained Level 2-variance is the amount of explained variance at the second level (here: the participants) by the predictor variables that are additionally included in a model (Hox, 2010).]. In Model 1, the intercept of remaining time was B0 = 2.887 (p < .001), which was slightly below the middle scale point. In Model 2, the baseline level of remaining time was B0 = 2.955 (p < .001). Remaining time decreased by Btime = −0.003 (p < .001) each month of the 4-year period and by Bage = −0.062 (p < .001) with each additional year of age of the partici-pant. In Model 3, the baseline level of remaining time was B0 = 2.705 (p < .001). Remaining time decreased by Btime = −0.003 (p < .001) each month and by Bage = −0.061 (p < .001) with each year of age of the participant. This model additionally included gender and education. Of these, education was significant (Beducation = 0.064; p < .001). That is, higher levels of education were associated with longer perceived remaining times. Women and men did not differ in perceived remain-ing time (Bgender = −0.004; p = .88). Thus, Hypothesis 1 was supported.

To explore Research Question 1, we added the cross-level interac-tion of time and age to Model 3 resulting in Model 4 (Table 3). This cross-level interaction was significant, although it was small (Btime × age = 0.0002; p < .001). Younger people differed from older people in the slope of their perceived remaining time such that younger adults exhib-ited a steeper decline in perceived remaining time across the 4 years than did older adults (Figure 1). A comparison of Models 1–4 revealed that Model 4 fit the data best because it had the lowest deviance and AIC and BIC.

To test Hypothesis 2, we applied the same procedure that was applied to Hypothesis 1 with the exception that the base model was a random intercept and random slope model (Table 4). In Model 1, the intercept of remaining opportunities was B0 = 3.083 (p < .001). In Model 2, the baseline level of remaining opportunities was B0 = 3.125 (p < .001). With each month of the 4-year period, remaining opportunities decreased by Btime = −0.002 (p = .004) and with each additional year of age of a participant, remaining opportunities decreased by Bage = −0.044

Table 2. Comparison of the Basic Models for Remaining Time and Remaining Opportunities

Remaining Time Remaining Opportunities

Model Unconditional Means

Random Intercept Random Intercept and Random Slope

Unconditional Means

Random Intercept Random Intercept and Random Slope

Model fit Pseudo-R2 81.5 % 82.1 % 75.3 % 77.4 %Deviance/-2LL 16,337.41 16,290.32***,a 16,287.76b 19,224.34 19,210.18***,a 19,193.82***,b

AIC 16,343.41 16,298.32 16,297.76 19,230.34 19,218.18 19,203.82BIC 16,364.16 16,325.99 16,332.34 19,251.09 19,245.85 19,238.40

Note. AIC = Akaike information criterion. BIC = Bayesian information criterion. Lower AICs, BICs and −2 log-likelihoods indicate better fits. Higher Pseudo-R2s indicate better fits.aThe significance level refers to the difference between the unconditional means model and the random intercept model.bThe significance level refers to the difference between the random intercept model and the random slope and random intercept model.*p ≤ .05, **p ≤ .01, ***p ≤ .001.



(p < .001). In Model 3, the baseline level of remaining opportunities was B0 = 2.476 (p = .001). With each month, remaining opportunities decreased by Btime = −0.002 (p = .003) and with each year of age of a par-ticipant, remaining opportunities decreased by Bage = −0.043 (p < .001). Both control variables (i.e., gender and education) were significant such that women and people with higher educational degrees perceived themselves as having more remaining opportunities—Bgender = 0.074; p = .043 and Beducation = 0.158; p < .001 [Please note that gender was not significantly related to remaining opportunities according to the bivari-ate correlations, but gender significantly predicted remaining oppor-tunities according to the multilevel analysis. The opposite pattern was observed for the association between gender and remaining time. This pattern might have been be caused by the fact that multilevel analyses

account for both within- and between-individual variations over time. Moreover, the female participants in the present study were better educated on average than the general population in Germany, which might have resulted in greater remaining opportunities (Statistisches Bundesamt, 2012).]. Taken together, people perceived themselves as having fewer remaining opportunities over time and with increasing age. Thus, Hypothesis 2 was upheld.

To explore Research Question 2, we added the cross-level interac-tion of time and age to Model 3, which resulted in Model 4 (Table 4). Model 4 revealed that the cross-level interaction of time and age was not significant (Btime × age = −0.000; p = .15). That is, younger people did not differ from older people in the slope of remaining opportunities. Overall, Model 3 fit the data best because this model exhibited the low-est significant deviance and the lowest AIC and BIC of the four models.

D I S C U S S I O NTo our knowledge, the present study is the first to test the stability of both dimensions of the OFTP: People do not only perceive themselves as having less and less time, but they also—to a lesser extent—perceive themselves as having fewer and fewer occupational opportunities left as their occupational time unfolds. This finding empirically supports the theoretical conceptualization of OFTP as a construct that changes across the working life from open-ended to constrained (Seijts, 1998; Zacher et al., 2009; Zacher & Frese, 2009). Concerning the perception of remaining time, the base model with the best trade-off between fit and parsimony was a random intercept model; that is, people’s percep-tions of remaining time developed in the same manner, but their initial level of remaining time varied. People might have adduced the official age of retirement as a reference point to gauge how much occupational time they have left. In agreement with decreasing perceived remain-ing time, perceived remaining opportunities also decreases over time. Concerning the perception of remaining opportunities, the random

Table 3. Multilevel Analysis of Remaining Time (Hypothesis 1 and Research Question 1)

Model M1: Null Model M2: + Time and Age M3: +Gender and Education

M4: + Time × Age Interaction

B SE B SE B SE B SE

Fixed part Intercept 2.887*** 0.020 2.955*** 0.015 2.705*** 0.050 2.714*** 0.050 Time −0.003*** 0.000 −0.003*** 0.000 −0.004*** 0.000 Age −0.062*** 0.001 −0.061*** 0.001 −0.066*** 0.001 Gender −0.004 0.025 −0.002 0.025 Education 0.064*** 0.012 0.063*** 0.012Time × Age 0.0002*** 0.000Random part σ 2

e 0.268*** 0.005 0.266*** 0.005 0.266*** 0.005 0.265*** 0.005 σ 2

intercept 0.741*** 0.025 0.239*** 0.010 0.235*** 0.010 0.234*** 0.010Model fit statistics Deviance/-2LL 16,337.41 14,262.10 14,210.15 14,173.17 AIC 16,343.41 14,272.10 14,224.15 14,189.17 BIC 16,364.16 14,306.68 14,272.55 14,244.48

Note. Gender: 0 = men; 1 = women. Education: 1 = no degree (yet); 2 = ninth-grade degree; 3 = tenth-grade degree; 4 = high-school diploma; 5 = master’s degree; 6 = doctorate. The predictor age is grand-mean-centered. The base model is a random intercept model. The Level 1-covariance structure and the Level 2-covariance structure are “identity” (ID). The intraclass correlation is ρ = 73.4%. Model 4 fit the data best because it exhibited the lowest significant deviance value. Further, this model explains R2

Level 1 = 1.1 % more Level 1-variance and R2

Level 2 = 68.4% more Level 2-variance than Model 1.*p ≤ .05, **p ≤ .01, ***p ≤ .001.

Figure 1. Interaction of remaining time and age (Research Question 1).



intercept and random slope model fit best; that is, people differed in both their initial perceptions of remaining opportunities and the devel-opment of these perceptions over time.

We assume that many factors determine the perception of remain-ing opportunities. One’s individual working conditions might affect the perception of remaining opportunities. For example, people who get a new supervisor, enter a new team, change their job, or switch their employer might suddenly perceive themselves as having more or less occupational opportunities. This explanation corresponds with research on the effects of work characteristics on perceived remain-ing opportunities: If a person performs a complex job or has as large quantity of job control, that person tends to perceive him- or herself as having more remaining opportunities (Zacher & Frese, 2009, 2011; Zacher et al., 2010). Therefore, in addition to an overall decrease in the perception of remaining opportunities across the occupational life, contextual factors might lead to interim peaks or dips. This phe-nomenon might also explain why the test-retest reliabilities of remain-ing opportunities were smaller than the test-retest reliabilities of remaining time.

As expected in Hypotheses 1 and 2, a higher age was associated with perceptions of less remaining time and fewer remaining oppor-tunities. These results support the notion that OFTP is an age-related construct (Zacher, 2013; Zacher & Frese, 2009, 2011; Zacher et al., 2010). Further, our findings extend previous knowledge about OFTP by quantifying the amount of the decrease in OFTP across 4 years. Furthermore, we found an interaction between time and age con-cerning the perception of remaining time via exploration; younger adults exhibited a steeper decline in their perceived remaining time compared to older adults. This finding breaks new ground in OFTP research because the interaction between age and time on OFTP has

not previously been explored. Because research on OFTP is in its infancy, we can only speculate on the reasons for which the OFTP of younger adults declined more steeply. Based on research on the FTP, younger people might face more endings of stages of life that decrease their perceived remaining time (Ersner-Hershfield, Mikels, Sullivan, & Carstensen, 2008). This finding accords with research on educa-tional and work transitions that has shown that these transitions pri-marily occur in early adulthood (Berntsen & Rubin, 2004; Settersten & Hagestad, 1996). Additionally, younger people might start a fam-ily, which is associated with breaks from work (George, 1993). These breaks might result in a decreased perceived remaining time at work. Similarly, our findings corroborate research on job change and job mobility as voluntary shifts “in and out the labor force as well as within and between employing organizations” (George, 1993, p. 252) decrease over the course of the lifetime (Krecker, 1994). For exam-ple, younger employees change their jobs every 2 years, whereas older employees change their jobs only every 6 years on average (Stettes, 2011).

The smaller decrease in remaining time among older people might not have only resulted from facing fewer (work) life transitions and job changes but might also have been driven by the positivity effect (Mather & Carstensen, 2005): As older people counteract the expe-rience of negative emotions, they might compare their constrained remaining occupational time with a comparably high remaining life-time instead of comparing it to their already elapsed occupational time. Therefore, older adults do not place themselves in a position to think about the approaching end of their working life, which might be a meaningful ending and thus associated with poignancy (Ersner-Hershfield et al., 2008); however, they might think about the length of time ahead as retirees, which would result in a positive future outlook.

Table 4. Multilevel Analysis of Remaining Opportunities (Hypothesis 2 and Research Question 2)

Model M1: Null Model M2: + Time and Age M3: + Gender and Education

M4: + Time × Age Interaction

B SE B SE B SE B SE

Fixed part Intercept 3.083*** 0.021 3.125*** 0.023 2.476*** 0.072 2.474*** 0.072 Time −0.002** 0.001 −0.002** 0.001 −0.002** 0.001 Age −0.044*** 0.002 −0.043*** 0.002 −0.041*** 0.002 Gender 0.074* 0.037 0.074* 0.037 Education 0.158*** 0.017 0.158*** 0.017 Time × Age −0.000 0.000Random part σ 2

e 0.414*** 0.025 0.414*** 0.025 0.417*** 0.025 0.417*** 0.025 σ 2

intercept 0.941*** 0.042 0.723*** 0.035 0.691*** 0.034 0.690*** 0.034 σ 2

slope 0.000*** 0.000*** 0.000*** 0.000 0.000*** 0.000 0.000*** 0.000Model Fit Statistics Deviance/−2LL 19,127.13 18,440.83 18,328.57 18,326.50 AIC 19,147.13 18,464.83 18,356.57 18,356.50 BIC 19,216.29 18,547.83 18,453.37 18,460.22

Note. Gender: 0 = men; 1 = women. Education: 1 = no degree (yet); 2 = ninth-grade degree; 3 = tenth-grade degree; 4 = high-school diploma; 5 = master’s degree; 6 = doctorate. The predictor age is grand-mean-centered. The base model is a random intercept and random slope model. The Level 1-covariance structure is “diagonal” (DIAG), and the Level 2-covariance structure is “unstructured” (UN). The intraclass correlation is ρ = 69.4%. Model 3 fit the data best; this model explained no more Level 1-variance but did explain R2

Level 2 = 26.6 % more Level 2-variance than Model 1. Further, Model 3 exhibited the lowest significant deviance value.*p ≤ .05, **p ≤ .01, ***p ≤ .001.



Strengths, Limitations, and Future ResearchOur results benefit from the large and heterogeneous sample and the longitudinal design. The present study considered a longer time span than has been considered in existing studies. As the OFTP scale was only published in 2009 (Zacher & Frese, 2009), we were not able to measure OFTP over a time span longer than 4 years. Although we would like to have examined OFTP over a longer period, the time frame of 4 years was sufficiently long to detect declines in perceived remain-ing time and opportunities. Although the effects of time on OFTP ini-tially seem small, one should consider that the beta values refer to the changes in remaining time and remaining opportunities each month. Moreover, the development of OFTP over time affects the entire labor force; therefore, even small effects are likely to be economically rel-evant. Nevertheless, it would be desirable for future research on the stability of OFTP to consider a longer time frame because the typical working life lasts approximately 40 years. Furthermore, we referred to the two dimensions of OFTP proposed by Zacher and Frese (2009). These authors distinguish the two dimensions of “remaining time” and “remaining opportunities.” Future studies should also consider whether the two-factor model that was proposed for general FTP by Cate and John (2007), which distinguishes between “focus on oppor-tunities” and “focus on limitations,” could also be adapted to OFTP.

Other longitudinal studies of the changes in both dimensions of OFTP over time and with age with which to compare our findings are lacking. However, we can refer to longitudinal studies of general FTP. Cate and John (2007) measured the FTP of 91 women at the age of 43 and again at the age of 61. These women’s focus on opportunities did not decrease, whereas their focus on limitations increased across 18 years. Kooij and colleagues studied FTP over 1 year and found a test-retest correlation of approximately r = .70 (Kooij et al., 2014; Kooij & Van de Voorde, 2011). Zacher and de Lange (2011) used a cross-lagged panel design to measure employees’ FTP (the subscales were focus on opportunities and focus on limitations) twice with a time lag of 3 months. These authors found that the employees’ focus on opportunities exhibited a test-retest correlation of r = .62 over time and that the employees’ focus on limitations exhibited a test-retest correla-tion of r = .50 over time. Our study of OFTP, which conceptualized a shorter time span because OFTP refers to one’s working life rather than one’s entire life, revealed test-retest correlations of r = .74 for remaining time and of r = .65 for remaining opportunities across 4 years. These results seem to compare well with the reported test-retest reliabilities for FTP. Moreover, our results can plausibly be extrapolated across a longer period of time. As the typical participant in this sample was 39 years old, he or she needed to work for approximately an additional 25 years. For remaining occupational time, the base level was 2.7 on a scale from 1 to 5, and the beta value for the effect of time was −0.004 for each month (Model 4 in Table 3). Therefore, in 25 years, one’s per-ceived remaining time will have decreased by approximately 1.2 points so that near the end of the working life, the perception of remaining occupational time will be as low as 1.5. For remaining occupational opportunities, the base level was 2.5. The effect of time was −0.002 for each month (Model 3 in Table 4). Consequently, the perception of remaining occupational opportunities would decrease by approxi-mately 0.6 scale points over 25 years, such that an average person would be expected to score 1.9. This plausibly implies that toward the official retirement age, perceived remaining occupational time is smaller than perceived remaining occupational opportunities. After being officially

retired, one might work in one’s own business or work reduced hours with one’s previous employer on a voluntary basis.

The effects of time and age on OFTP can be estimated if we con-sider the variance explained by time and age. For remaining occu-pational time, time and age (Model 3) explain 68.3 % more Level 2-variance (Model 3) than the null model. For remaining occupational opportunities, time and age (Model 3) explained 26.6 % more Level 2-variance than the null model. As Level 2-variance is interpreted as analogous to multiple R2 (Hox, 2010), time and age have good pre-dictive powers for OFTP. Furthermore, the effects of time and age on remaining time were also significant when we considered a smaller subsample (i.e., people for whom data about OFTP were available for all six measurement points; the results of the data on OFTP at all six measurement points are available as supplementary material. Please contact the first author).

Due to the requirements of the online panel, the time intervals between measurement points varied from 5 to 20 months in our study. However, in multilevel models, spacing between measurement points is allowed to vary between participants without markedly affecting the results (Bryk & Raudenbush, 1987; Hox, 2010). Additionally, demand effects might have been more likely had we assessed OFTP at specific time intervals. Moreover, the numbers of OFTP measurement points differed between participants. Nearly 60% of our sample had two or three OFTP measurement points, whereas approximately 20% of our sample had five or six OFTP measurement points. However, multi-level models also allow the numbers of measurement points to vary between participants without markedly affecting the results (Bryk & Raudenbush, 1987; Hox, 2010).

Regarding the sample characteristics, the participants were German-speaking, and we have no data about their (sub)cultural back-grounds or ethnicities. The generalizability of our findings might have been enhanced by testing the stability of OFTP cross-culturally because the concept of time differs across (sub)cultures and ethnic groups (e.g., polychronic vs. monochronic time perception; Hall, 1959). Further, the lawful retirement age differs across countries. For example, France and Austria have a legal retirement age of 60 years, whereas the United States has a legal retirement age of 66 years (Statista, 2013). Therefore, people living in different countries differ in the duration of their work-ing lives. People in France and Austria might thus perceive their OFTP to decrease faster than people in the United States.

Regarding age, the average study participant was 39 years old (i.e., middle-aged). Approximately two-thirds of the sample was between 28 and 50 years old, which demonstrates that the sample was het-erogeneous in age. Nonetheless, middle-aged people were somewhat overrepresented in our sample, whereas comparatively young (under 28 years of age) and comparatively old (51 years of age and older) people were underrepresented. Consequently, the actual effect might even be more pronounced, and this issue could be further elucidated by studies of OFTP among more very young and very old employees.

People with no educational degree (yet) or a ninth-grade degree were underrepresented in this study compared to the German popula-tion (no educational degree (yet): 7.5% in the population vs. 0.4% in our sample; ninth-grade degree: 35.6% in the population vs. 8.2% in our sample). However, in terms of higher education, the sample com-pared well to the general population (tenth-grade degree: 29.0% in the population vs. 26.9% in our sample; high-school diploma: 27.3% in the population vs. 29.0% in our sample; Statista, 2012). As a higher level of



education was associated with the perceptions of longer remaining time and more remaining opportunities, the high education level in our sam-ple might have reduced the negative effects of time on OFTP; that is, the actual decline of OFTP as people grow older might be even larger.

Although we controlled for gender and education as potential con-founding variables, we were unable to control for the contextual fac-tors or life events that the participants faced over the 4 years, which might have affected their changes in OFTP. One context factor might be a person’s health status because better subjective health is not only associated with a more open-ended general FTP (Kooij & Van de Voorde, 2011) but also with OFTP. Better physical health is associated with both more open-ended remaining occupational time and more remaining occupational opportunities. Moreover, better mental health is associated with more open-ended remaining occupational time (Zacher & Frese, 2009, 2011). As we excluded all unemployed and (early) retired people, we assume that people with severely impaired health and, as a result, strongly decreased OFTP were not included in the study due to self-selection.

Other unknown context factors include (a) people’s professions or occupational positions and (b) their jobs or organizational tenures. Both of these factors might affect the extents to which job complexity and job control are assigned to an employee. For example, an employee might be assigned higher job complexity and higher job control if he or she has accumulated more work- and organization-related information due to greater job and organizational tenure. In turn, these job charac-teristics affect the perception of remaining opportunities. For example, people who exhibit high job control and perform complex jobs are more likely to maintain a perception of many remaining opportunities (Zacher & Frese, 2009, 2011; Zacher et al. 2010). As typical retire-ment ages differ somewhat across industries, the industry might also affect the perception of remaining time.

Concerning life events, we did not assess whether people changed their jobs or employers across the study period. On the one hand, job changes might be associated with a constrained future time in the cur-rent organization, which might decrease people’s OFTP. On the other hand, people might perceive increased remaining opportunities after entering a new job or organization. Another fruitful area of research might be to explore how longer breaks in working life (e.g., parental leaves or sabbaticals) affect the change of OFTP in the long term. We assume that these time-outs shorten the perception of remaining occupational future time because people “lose” occupational time. However, it is also possible that people perceive themselves as having more remaining opportunities in their occupational future because they are not committed to a traditional career path and have widened their horizon during the time-out. We recommend future research to explore the effects of these context factors on OFTP.

Concerning period effects, we should note that this study took place from 2009 to 2013. Beginning in autumn 2008, the financial crises had repercussions on the German labor market (e.g., job cuts and the intro-duction of short-time work/reduced work hours; Bundesagentur für Arbeit, 2009). These economic circumstances might have negatively affected the employees’ perceptions of OFTP. Further, the employees’ perceptions of remaining time and opportunities might have been influenced indirectly by acquaintances or relatives who were affected by unemployment or short-time work due to the crisis. However, short-time work peaked in February 2009, and only 5.2% of employees were involved in short-time work in June 2009, while the study began

in November 2009. Despite the overall decrease in perceived remain-ing opportunities over time, we observed a small increase from t2 to t5. This increase might have been occurred because the German economy has increasingly recovered from the crisis since 2009. Although we conclude that period effects did not threaten the validity of our results, we advise replicating these findings. Overall, we recommend future research on OFTP to gain a deeper insight into workplace behaviors.

Practical ImplicationsFrom a practical perspective, an open-ended OFTP is associated with positive outcomes for employers and employees (Gielnik et al., 2012; Schmitt, Zacher, et al., 2013; Zacher et al., 2010). To maintain the perception of an open-ended remaining occupational time, a coun-try’s social policy might soften or suspend extant legal retirement ages. Additionally, we recommend that employers invest in maintaining an open-ended OFTP among their employees across the entire occupa-tional lifespan. Employers could offer employees the possibility to work voluntarily beyond the retirement age, or they could offer bridge-employment. Consequently, employees might not perceive that the end of their working life is fixed. For example, organizations could imple-ment a web space in which employees can sign in during their working life and in which they can be committed over and above their working life. Via this web space, retired employees could, for example, be availa-ble as mentors and could thus still play an active role in the working life.

We tentatively recommend that employers prevent making the endings of career stages salient. This recommendation holds especially for younger people because we observed a steeper decline in the per-ception of remaining time among younger people. Superiors should attempt to regard the working life as a whole instead of as a cascade of separate career stages because the latter is associated with facing many endings. For example, supervisors could use annual appraisal inter-views to bring this continuous view to their employees’ attention when discussing their occupational future.

To maintain the perception of many remaining occupational oppor-tunities in employees of all ages, a country’s social policy should ensure that no age discrimination occurs in human resource management practices. This step is not only important for fostering the perception of many remaining occupational opportunities across the lifespan but also for enabling older people’s participation in the labor force, which will be increasingly important in the face of demographical changes. Similarly, employers are well advised to provide (new) occupational opportunities for their employees. Zacher and Frese (2009) found that job complexity compensates for the negative effects of age on perceived remaining opportunities. Hence, it might be worthwhile to redesign low-complexity jobs, for example, by incorporating new tasks. This approach might also apply to workers with lower educational degrees for two reasons. First, we found that lower levels of education were associated with fewer perceived remaining occupational opportunities. Second, workers with lower educational degrees tend to perform jobs that are low in complexity or control. Supervisors could, for example, use the annual appraisal interview to exhibit opportunities, goals, and plans that an employee can put into action in the next year.

C O N C L U S I O NThis study contributes to the understanding of the stability and change of OFTP over time. In accordance with previous research, we found



evidence for distinguishing two dimensions of OFTP: remaining time and remaining opportunities. People continuously change their views on their occupational future as they approach retirement age; i.e., peo-ple not only perceive themselves as having less and less time left but also, to a lesser extent, perceive themselves as having fewer and fewer opportunities left in their occupational future. Regarding perceived remaining occupational time, the rate at which perceived remaining time shrinks over time depended on the employee’s age: Younger people exhibited a steeper decline than did older people. Given the ageing of the working population in most industrialized nations, age-related issues are increasingly important topics in work and organiza-tional psychology. As we found that OFTP decreased over time, and other researchers have found that an open-ended OFTP is associated with positive work-related outcomes, we recommend that employers uphold their employees’ OFTP. By assigning challenging tasks to all employees, employers can prevent strong decreases in the perception of remaining opportunities. Furthermore, employers could prevent making endings in working life salient. In general, we recommend that future research sheds light on age-related issues in work and organi-zational psychology by considering OFTP to achieve a deeper under-standing of workplace behaviors.

S U P P L E M E N TA R Y M AT E R I A LSupplementary material can be found at: http://www.oxfordjournals.org/our_journals/workar/

A C K N O W L E D G M E N T SWe thank Associate Editor Donald M. Truxillo and two anonymous reviewers for their helpful and constructive suggestions.

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