1. Introduction well. A number of new constructs have been ...Previous research revealed that the determinants of objective vs. subjective success Self-Regulation and Career Success

Self-Regulation and Career Success - 1 -

1. Introduction

The job market is undergoing large-scale changes (e.g., globalisation, declining job se-

curity, discontinuous employments, life-long learning), and individual careers are changing as

well. A number of new constructs have been introduced to account for these changes, such

as the concepts of boundary-less career (see Arthur, Khapova, & Wilderom, 2005; Arthur &

Rousseau, 1996) or protean career (Hall, 2002). A common core in these different conceptu-

alisations of contemporary forms of occupational careers is the assumption that there is a

high need for individuals to actively manage their careers by setting themselves goals and

trying to attain those goals (Allred, Snow, & Miles, 1996; Murphy & Ensher, 2001). It is there-

fore not suprising that interest in conceptualising individual careers from a perspective of

self-regulation is growing (e.g., Kanfer, 2005; King, 2004; Vancouver, 2000; Vancouver &

Day, 2005; Wood, 2005; Zimmerman, 2000). Despite the current popularity of the self-

regulatory perspective respective research on career success is scarce. A recent meta-

analysis on determinants of career success (Ng, Eby, Sorensen & Feldman, 2005) listed not

a single study on this topic.

Self-regulation refers to thoughts, feelings, and actions that are planned and adapted to

the attainment of personal goals (Zimmerman, 2000). The idea that people have the power to

actively control their careers through such purposeful thought is fascinating (e.g. Bandura,

1997). The present research empirically addresses this idea. We consider two central com-

ponents of self-regulatory thoughts, namely beliefs in own capacities and mastery versus

prestige related career goals.

Self-regulation, however, is not a one-way process from thought to action but rather a

cyclical one (Vancouver, 2000; Vancouver & Day, 2005; Zimmerman, 2000). Goals and be-

liefs not only influence behaviors and outcomes; behaviors and outcomes affect goals and

beliefs as well. Accordingly, the second purpose of the present research is the analysis of a

reciprocal influence of self-regulation and career success.

What is career success? Is it money and promotion or is it satisfaction and positive

evaluation? In order to adequately address the questions we posed above, the complex con-


struct of career success has to be defined more thoroughly. Career success refers to “the

real or perceived achievements individuals have accumulated as a result of their work ex-

periences” (Judge, Higgins, Thorensen, & Barrick, 1999, p. 621). Objective career success

reflects verifiable attainments like pay, position, and promotions. Subjective career success

emphasizes the beholder’s subjective evaluation (cf. Dette, Abele & Renner, 2004; Heslin,

2003, 2005; Judge, Cable, Boudreau & Bretz, 1995; Nicholson & De Waal-Andrews, 2005).

Do self-regulatory thoughts influence subjective success, objective success, or both compo-

nents? Do reciprocal influences of career outcomes on self-regulatory thoughts emerge for

both objective and subjective outcomes or are they limited to one of them?

In sum, we are interested in the career determinants that are in our heads, and in how

purposeful thought influences career progression. More specifically, we are interested in the

influence that our work-related beliefs and goals have on career progression, and in how

they are affected by objective and subjective career success. Knowing more about such de-

terminants and consequences of career success is of interest for theorizing on self-

regulation, it is of interest for theorizing on career development, and it may help to resolve

questions of career planning, career counseling, and personnel selection. We will test our

assumptions in a prospective three-year, three-wave longitudinal study with a large sample

of professionals from different occupational fields.

1.1 Self-Regulation, Performance, and Career Success

According to Bandura (1986, 1997, 2001), self-regulation is initiated by personal goal

setting and comprises self-related processes of observation, evaluation, and reaction (see

also Carver & Scheier, 1990; Karoly, Boekaerts & Maes, 2005; Vancouver, 2000; Vancouver

& Day, 2005; Wood, 2005; Zimmerman, 2000). Goals as aims of an action (Locke & Latham,

2002) or internally represented desired states (Austin & Vancouver, 1996) are hierarchically

organized and range from biological set points (e.g., body temperature) to complex cognitive

depictions of desired outcomes (e.g., career success). Goals influence outcomes by directing

attention, mobilizing effort, affecting persistence, and structuring behavior. Goals predict per-

formance above and beyond cognitive ability (e.g. Austin & Vancouver, 1996; Locke &


Latham, 2002; Payne, Youngcourt, and Beaubien, 2007). The present question about the

impact of goals on career success refers to the content of goals. Despite many differences,

most conceptualizations of goal content share one core distinction that is of relevance here

(cf. Austin & Vancouver, 1996; Dweck, 1986; Furnham, Petrides, Tsaousis, Pappas & Gar-

rod, 2005; Hazer & Alvares, 1981; Locke & Latham, 2002; Nicholls, 1984; Super, 1957; Zy-

towsky, 1994). It is the distinction between more “intrinsic”, mastery, learning, growth, and

intellectual stimulation related goals and goal orientations vs. more “extrinsic”, power, per-

formance prove, materialistic and prestige related goals and goal orientations (Dweck, 1986;

Furnham et al., 2005; Hazer & Alvares, 1981; Locke & Latham, 2002; Nicholls, 1984; Super,

1957, 1970; VandeWalle, 1997; VandeWalle, Brown, Cron & Slocum, 1999). We will draw on

this distinction here.

Another central component of self-regulation are beliefs in one’s capacity to perform

some behavior or to meet a standard, e.g., self-efficacy beliefs (Bandura, 1986, 1997). Ac-

cording to Bandura, individuals with high self-efficacy beliefs set higher goals for themselves,

put in more effort, and persist longer on a difficult task. Self-efficacy beliefs significantly con-

tribute to the level of motivation and performance above and beyond cognitive ability (Ban-

dura & Locke, 2003).

1.2 Self-efficacy, goal content, performance, and success

Because there is only very little research on self-efficacy, goal content, and career suc-

cess, we will briefly consider research on the related issue of performance. Meta-analyses on

cross-sectional and experimental studies show medium size positive correlations of self-

efficacy with task performance (Bandura & Locke, 2003; Judge & Bono, 2001; Judge, Jack-

son, Shaw, Scott & Rich, 2007; Locke & Latham, 1990; Sadri & Robertson, 1993; Stajkovic &

Luthans, 1998). Cross-sectional field studies revealed that self-efficacy is associated with,

e.g., sales performance (Barling & Beattie, 1983), research productivity (Kahn & Scott, 1997;

Vasil, 1992), job performance (Day & Allen, 2004; Lubbers, Loughlin & Zweig, 2005), and

external performance ratings (Riggs, Warka, Babasa, Betancourt & Hooker, 1994; Judge,

Thoresen, Pucik & Welbourne, 1999). However, a meta-analysis by Judge et al. (2007) sug-


gests that the effects of self-efficacy on performance might be much weaker when individual

difference measures are taken into account. Schwoerer and May (1996) found no effect of

employees’ self-efficacy on external performance ratings, and recent research by Vancouver

and Kendall (2006) as well as Yeo and Neal (2006) even found a negative influence of self-

efficacy on performance if measured on an intra-individual level. Brett and VandeWalle

(1999) found that both goal orientation and goal content predicted performance in a training

program. Research by Harackiewicz and colleagues (Harackiewicz, Barron, Carter, Lehto &

Elliot, 1997; Barron & Harackiewicz, 2001) tested the influence of multiple goals and re-

vealed that both learning goals and performance goals were important for performance and

optimal motivation.

Regarding career success, two cross-sectional studies reported a relationship between

self-efficacy and indicators of objective career success, e.g., pay and hierarchical position

(Day & Allen, 2004; Lubbers, Loughlin & Zweig, 2005). Longitudinal research by Saks (1995)

revealed that task-related self-efficacy had a positive effect on job satisfaction. Frieze, Olson,

Murrell, and Selvan (2006) showed that both materialistic, prestige-related work values (e.g.,

wanting to be recognized in one’s field, wanting high pay) and achievement-related work

values (e.g., wanting to do an excellent job) predicted later salary. Abele and Stief (2004)

found that people with higher self-efficacy expectation were more successful in their career

entry than those with lower self-efficacy expectations.

1.3 Reciprocal influence of performance and success on self-efficacy and goals

The theoretical notion of a feed-forward mechanism suggests that goals lead to activi-

ties for attaining them, and that goal-attainment leads to adapted goal-setting and adapted

self-evaluation which instigate further efforts to attain the adapted goals (Bandura, 1986,

2001; Bandura & Locke, 2003). Hence, self-efficacy and goals should both be somewhat

stable, but also malleable by situational influences and long-term experiences. Research

shows that self-efficacy can be altered directly and without behavioral feedback (cf. Bandura

& Locke, 2003). However, behavioral feedback has an influence as well. Self-efficacy beliefs

increase after success and decrease after failure (cf. Cervone et al., 2004; Shim & Ryan,


2005; Smith, Kass, Rotunda & Schneider, 2006). Stability coefficients of self-efficacy vary

from .57 to .90 depending on the time interval between measures (Dormann, Fay, Zapf &

Frese, 2006; Shim & Ryan, 2005). Goals are adjusted to performance feedback (difficulty:

Ilies & Judge, 2005; goal orientation: Radosevich, Vaidyanathan, Yeo and Radosevich,

2004). However, we know little about the malleability of goal content (e.g. Hazer & Alvares,

1981; stability coefficients for goal orientations at time intervals ranging from 1 to 14 weeks

are about .60, Payne et al., 2007).

2. Present Research

The studies summarized so far provide some evidence that self-efficacy has an impact

on performance, even though the evidence is less conclusive than one might expect given

the prominence of the construct. There is also research showing that goal content matters. In

some contexts “intrinsic” goals seem to have more influence on performance, but in many

other contexts both “intrinsic” and “extrinsic” goals are important. Finally, there is some evi-

dence for a reciprocal influence of performance on self-efficacy and on goal attributes.

Evidence regarding the relationship between self-efficacy, goal content, and career suc-

cess is very limited (Abele & Stief, 2003; Day & Allen, 2004; Frieze et al., 2006; Lubbers et

al., 2005; Saks, 1995). Almost no research has addressed a longitudinal influence of self-

efficacy on objective career success. This seems especially warranted since the research

cited above (Vancouver & Kendall, 2006; Yeo & Neal, 2006) showed that self-efficacy may

also have a negative influence on performance if it is measured in a within-participants de-

sign. One study found a longitudinal influence of goal content on career success (Frieze et

al., 2006). This evidence is not enough to firmly establish whether “intrinsic” and “extrinsic”

goal content are equally important in predicting career success or whether one is more im-

portant than the other. Moreover, there are no studies analyzing the influence of self-efficacy

and goal content on both objective and subjective aspects of career success. However, a

differentiation between these different facets of success is important for our general question

whether self-regulatory thoughts mainly have an influence on subjective parameters of suc-

cess. Previous research revealed that the determinants of objective vs. subjective success


are somewhat different (Dette et al., 2004; Ng et al., 2005) and that subjective success is

more strongly tied to individual difference variables than objective success. Finally, data on

the reciprocal influence of career success on self-efficacy and goal content are lacking. The

aim of the present study is to fill these research gaps.

For the present research we conceptualize objective career success by pay and respon-

sibility level. Subjective career success can be differentiated further (Dette et al., 2004; Hes-

lin, 2003, 2005). Self-referent subjective success—the most common conceptualization of

subjective success—refers to an evaluation against own standards, for instance satisfaction

with one’s career (Greenhaus, Parasuraman, & Wormley, 1990). Other-referent subjective

success refers to a comparison of one’s career with a comparison standard, for instance the

career of another person or group (see Dette, et al., 2004; Heslin, 2003; Turban & Dough-

erty, 1994). This latter approach has only rarely been used despite the fact that people have

a tendency to compare their actions and outcomes to those of other people, especially in

areas in which they have to evaluate a “social” reality (Festinger, 1954). The present re-

search will employ such an other-referent measure of subjective success.

Since we are interested in outcomes related to occupational career, we will not analyze

general self-efficacy, but rather occupational self efficacy (Abele, Stief & Andrä, 2000) which

is on a medium level of specificity. A medium level of specificity is advantageous in predicting

specific outcomes (Chen, Gully & Eden, 2001; Pajares, 1996).1 Regarding the two classes of

goal content that we broadly distinguished above, we conceptualize goals in the tradition of

work values (Super, 1970; Zytowski, 1994). Mastery goals comprise goals related to intellec-

tual stimulation and growth. Materialistic and prestige-oriented goals comprise goals directed

at pay, promotions, and upward mobility. Finally, because we are interested in the pure ef-

fects of self-efficacy beliefs and occupational goals on career success, it is important to con-

trol for individual differences in performance level. Hence, we will also consider our partici-

pants’ grade point average (GPA) as a control variable.

Figure 1 graphically illustrates the hypotheses. We assume that occupational self-

efficacy has a positive influence on both objective and subjective career success and that


both classes of goal content have a positive influence on carreer success as well. Further-

more, we assume that these self-regulatory variables have a direct influence on objective

career success that is not mediated by the subjective perception of success. We further as-

sume that occupational self-efficacy and mastery goals are correlated (see Payne, et al.,

2007; Seijts, Latham, Tasa, & Latham, 2004).

Hypothesis 1. Occupational self-efficacy influences objective career success.

Hypothesis 2. Mastery goals influence objective career success.

Hypothesis 3. Materialistic, prestige-oriented goals influence objective career success.

Hypothesis 4. Occupational self-efficacy influences subjective career success.

Hypothesis 5. Mastery goals influence subjective career success.

Hypothesis 6. Materialistic, prestige-oriented goals influence subjective career success.

Regarding reciprocal influences one could argue that only subjective success matters,

because people will only adapt their beliefs and goals, if they feel successful. However, one

could also argue that objective success matters, because it is the basis for adaptation. We

will test the hypothesis that both facets are important.

Hypothesis 7. Objective career success leads to changes in occupational self-efficacy, mas-

tery goals, and prestige goals.

Hypothesis 8. Subjective career success leads to changes in occupational self-efficacy,

mastery goals, and prestige goals.

2.1 Objective and subjective career success

Although objective and subjective career-related outcome criteria tend to be positively

correlated, the size of these correlations is moderate at best. The meta-analysis by Dette et

al. (2004) revealed an estimated correlation of .28 between objective career success and

subjective career success. Similarly, the meta-analysis by Ng et al. (2005) found a correla-

tion of .30 between pay and career satisfaction, and a correlation of .22 between promotions

and career satisfaction. Our research allows a test of whether the relationship between both

variables is merely correlational or whether there is a directional influence. If objective career

success and subjective career success are merely correlated, then the longitudinal influence


of objective career success on subjective career success should be completely mediated by

prior subjective career success, and the longitudinal influence of subjective career success

on objective career success should be completely mediated by prior objective career suc-

cess. However, if there is a directional influence of objective career success on subjective

career success or vice versa, then there should be no mediation.

2.2 Gender

It is critical for research on career success to consider gender, because women’s and

men’s career experiences are different; in general women are objectively less successful

than men (Abele, 2003; Greene & DeBacker, 2004; Kirchmeyer, 1998; Ng et al., 2005). Our

present aim is not to fully explain why women, on average, are objectively less successful

than men. We rather strive to analyze possible mediating effects of occupational self-

efficacy, mastery goals, and prestige goals. In the past, women tended to have lower self-

efficacy than men (Betz & Fitzgerald, 1987), but this difference has become smaller over

time (Bandura & Wood, 1989; Greene & DeBacker, 2004; Philips & Imhoff, 1997; Schwoerer

& May, 1996; Silver, Mitchell & Gist, 1995). Findings on gender differences in work related

goals are inconclusive (Frieze et al. 2006; Furnham et al., 2005).

Hypothesis 9. Gender has a direct effect on objective career success: Women are less suc-

cessful than men.

Hypothesis 10. If there are gender differences in occupational self-efficacy, mastery goals,

and/or prestige goals, they should partly mediate women’s lower objective career success.

We assume smaller or even no gender differences in subjective career success. Accord-

ing to the shifting standards model (Biernat & Billings, 2001), the use of subjective scales

leads to reduced differences between groups due to the application of different anchors.

Women may implicitly use a lower anchor in assessing their success than men. Using a

lower anchor, however, leads to similar assessments of subjective success despite lower

objective success.

3. Method

3.1 Overview


We tested our hypotheses with data collected in a prospective longitudinal study with a

large sample of professionals who had graduated from a German university (see also Abele,

2003; Abele & Stief, 2003). Two cohorts of graduates completed the first questionnaire some

weeks after they had passed their final exams. They received the second questionnaire 17

months later and the third one 3 years after graduation. We conducted the present research

with a selection of the measures taken in these three questionnaires.

3.2 Participants and Procedure

Participants were representative with respect to gender, study major, and GPA for the

two cohorts we looked at. We did not find any cohort effects or time of measurement effects

(cf. Palmore, 1978). Therefore, we report results for the combined data from both cohorts.

Because of data protection directives, the first questionnaire could not be sent directly to

the graduates. Instead, we had to display the questionnaires in administration offices. All

graduates had to visit these offices in order to collect some official certificates. Together with

these certificates they received the first questionnaire. The graduates were also asked to

provide their address, because the study would be continued some time later. From the

4,200 questionnaires displayed 1,930 (46%) were sent back to the researchers.

Time 1. Participants were 825 women and 1,105 men (mean age 27 years). They were

predominantly German; about 5% came from other European countries. Ninety-four percent

of the respondents provided their address (N = 1,819). Among other variables, we assessed

gender, GPA, study major, occupational self-efficacy, mastery goals, and prestige goals at

time 1. Participants who provided their address did not differ from participants who declined

to provide their address with regard to these variables.

Time 2. 102 of the 1,819 participants who had provided their address in the first ques-

tionnaire had moved to an unknown address at time 2. Of the remaining 1,717 persons,

1,398 participants (589 women and 809 men; mean age 28.5 years) responded to the sec-

ond questionnaire (response rate 81.4%). A drop-out analysis revealed that there were no

differences (gender, age, study major, GPA, time 1 self-regulation variables) between par-

ticipants who answered the second questionnaire and those who did not. At time 2, we


measured mastery goals, prestige goals, objective career success, and subjective career

success among other variables.

Time 3. Of the 1,663 participants who could be contacted three years after graduation

(54 individuals had moved to an unknown address), 1,330 (561 women, 769 men; mean age

30 years) responded to the third questionnaire (response rate 80%). There were again no

differences (same variables tested as at time 2) between participants who answered the third

questionnaire and those who did not. Among other variables, we measured occupational

self-efficacy, objective career success, and subjective career success at time 3.

As can be seen from this description, we collected data on all self-regulation variables at

time 1, measured goals again at time 2, and assessed occupational self-efficacy again at

time 3. This partial asymmetry in measurement is due to the fact that the research which we

took our data from was not exclusively designed for testing the present hypotheses, and that

these questionnaires measured a large number of additional variables.

The following analyses were performed with the 1,219 participants (516 women, 703

men) who completed all three questionnaires. They had graduated in law (29 women, 37

men), medicine (88 women, 113 men), arts and humanities (98 women, 37 men), natural

sciences (49 women, 101 men), economics (80 women, 130 men), engineering (18 women,

205 men), and teaching (154 women, 80 men).

3.3 Measures

3.3.1 Occupational self-efficacy

The scale we used to measure occupational self-efficacy (Abele, Stief & Andrä, 2000)

consists of 6 items (sample items “I am confident that I could deal efficiently with the chal-

lenges of my work if I only want to“; “I doubt that I really have the skills necessary for my

work”, reversely coded). Participants respond on 5-point scales (1 = not at all to 5 = very

much). The occupational self-efficacy scale is one-dimensional and shows good internal

consistency (Abele et al., 2000). For the present sample the internal consistency was Cron-

bach’s α = .78 (both at time 1 and time 3).

3.3.2 Goals


The scales we used to measure mastery goals and materialistic, prestige-related goals

consist of 4 items each (mastery goals: “I want to work on difficult and challenging tasks”; “I

want to improve my competencies”; “I want to contribute to innovations”; “I want to continu-

ously broaden my mind”; materialistic, prestige-related goals: “I want to make a lot of

money”; “I want to gain high occupational reputation”; “I want to have good career opportuni-

ties”; “I want to gain high social prestige”). The item formulations were adapted from a Ger-

man version of the work values inventory (Super, 1970; German version: Seifert & Berg-

mann, 1983). Participants respond on 5-point scales (1 = not important to 5 = very impor-

tant). In a pretest with 147 students (90 women, 57 men; mean age 23 years) the two factors

clearly emerged (factor I: materialistic, prestige oriented goals, 32% explained item variance,

Cronbach’s α = .77; Factor II: mastery goals, 25% explained item variance, Cronbach’s α =

.63). In the present sample this two-factorial structure was replicated (factor I, materialistic,

prestige-oriented goals, 30% explained item variance, Cronbach’s α = .71; Factor II: mastery

goals, 20% explained item variance, Cronbach’s α = .60). The alpha coefficient for mastery

goals is rather low. However, the items measuring mastery goals were designed to tap dif-

ferent facets of the construct. Internal consistency might not be an optimal estimate of reli-

ability for such heterogeneous subscales (test-retest correlation see Table 1).

3.3.3 Objective career success

Objective career success was measured by an index we developed for the current study

to be applicable to all participants and to every time of measurement. The index was

weighted to balance pay (weighted by two thirds: maximally 11 points) and responsibility

status (weighted by one third: maximally 5.5 points). It was composed of the following crite-

ria: (a) monthly pay before taxes (in thirteen steps from “no income”, coded as 0; “less than

€500.-”, coded as 0.5 [1 € about 1.30 $]; “less than €1,000”, coded as 1; “less than €2,000.-”,

coded as 2; and then in equal steps to “less than €10,000”.-, coded as 10; and “more than

€10,000”; coded as 11) 2, (b) permission to delegate work (0 = no, .92 = yes), (c) project re-

sponsibility (0 = no, 1.83 = yes), and (d) leadership position (0 = no, 2.75 = yes). Hence, the

index could vary between 0 (low success) and 16.5 (high success). We assigned zero points


to participants who had been without employment for at least three months before answering

the questionnaire.

We tested the factor structure of our objective career measure by means of confirmatory

factor analyses for categorical variables using the WLSMV estimator (using Mplus; Muthén &

Muthén, 2004). The results supported a one-factor structure (t2: χ2 < .01, p = 1.00, WRMR

=.001, CLI = 1.00, TLI = 1.00; t3: χ2 = 8.00, p < .01, WRMR = .94, CLI = 1.00; TLI = .99).

3.3.4 Subjective career success

We operationalized other-referent subjective career success as a comparison with for-

mer fellow students (“Compared with your former fellow students, how successful do you

think your career development has been so far?”), because pretests had shown that at our

participants’ present career stage former fellow students were highly significant comparison

targets. Participants based their responses on a 5-point rating scale (1 = less successful to 5

= more successful). Whereas one-item measures are usually suboptimal, in the present case

the single item captures the essence of other-referent career success that we wanted to as-

sess (cf., Heslin, 2003).

Participants who were currently not employed (and had not been for at least 3 months)

(continuing education, unemployment, parental leave etc.) did not answer this question (time

2: 69 participants; time 3: 114 participants). The analysis of subjective career success is

therefore based on 1,060 participants who had been employed both at time 2 and time 3.

3.3.5 Grade point average

We standardized individual GPA’s to the respective year’s and respective major’s av-

erage GPA. A value of “0” means average, a positive value means above, and a negative

value means below average GPA.

4. Results

4.1 Preliminary Analyses

4.1.1 Grade point average

Our participants’ mean GPA was close to the overall average, M = .04 (SD = .51). The

correlations between GPA and the self-regulation variables were low, but due to the large


sample size, two of them were significant (occupational self-efficacy r = .08, p < .01; mastery

goals r = .07, p < .05; prestige goals r = .04, ns). Despite these low correlations we tested

whether the findings we report later regarding influences of self-regulation on career success

would change if GPA was included. This was not the case. 3

4.1.2 Study major.

Our participants had different study majors, and the gender by study major distribution

was not even. It was therefore necessary to test main effects, and especially interactions of

study majors with gender and with the self-regulation variables, with respect to the depend-

ent measures.

To test for possible interactions of study major with the self-regulation variables in pre-

dicting career success, and to test for possible interactions of study major with career suc-

cess in predicting the subsequent self-regulation variables changes we computed regression

analyses in which we regressed the four success measures (objective career success time 2,

time 3; subjective career success time 2, time 3) on study major (dummy-coded), the self-

regulation variables, and the interactions of the self-regulation variables with study major,

and we computed regression analyses in which we regressed the three self-regulation vari-

able changes (time 2 goals, time 3 occupational self-efficacy) on study major (dummy

coded), career success (both objective and subjective) and the interactions of study major

with career success. Of the resulting 108 interactions 12 were marginally significant and they

always explained less than 1% of variance. We therefore conclude that these interactions are

negligible.

To test for possible interaction effects of study major and gender on career success we

computed analyses of variance (ANOVAs) with study major and gender as factors and the

four success measures as dependent variables. Study major had a highly significant influ-

ence on objective career success at time 2, F (6, 1205) = 81.66, p < .001, η2 = .29, and on

objective career success at time 3, F (6, 1205) = 41.24, p < .001, η2 = .17, but it did not inter-

act with gender, both Fs < 1.64, ns. Study major had an influence on time 2 subjective career

success, F (6, 1046) = 4.17, p < .001, η2 = .023, but not on time 3 subjective career success,


F < 1, and there were no interactions with gender, both F < 1. Graduates with majors in eco-

nomics and engineering had higher scores than the other graduates both in time 2 and time

3 objective career success and in time 2 subjective career success.

Regarding influences of study major and gender on the self-regulation variables, occu-

pational self-efficacy slightly differed between study majors, F (6, 1205) = 3.03, p < .01, η2 =

.015, with lower occupational self-efficacy of teachers (M = 3.60) than engineers and partici-

pants with majors in arts and humanities (both M = 3.90), with the other subjects falling in-

between (economics M = 3.77; medicine M = 3.75; law M = 3.69; natural sciences M = 3.66).

Mastery goals differed between study majors, F (6, 1205) = 8.59, p < .001, η2 = .041, with

lower values of law graduates (M = 3.47) and teachers (M = 3.56) than graduates from medi-

cine (M = 3.77), economics (M = 3.78), and arts and humanities (M = 3.80), and with highest

values of natural sciences graduates (M = 3.89) and engineers (M = 3.90). Prestige goals

also differed between study majors, F (6, 1205) = 12.69, p < .001, η2 = .059 (graduates from

law: M = 3.42; economics: M = 3.24; medicine: M = 3.04; engineers: M = 3.03; arts and hu-

manities: M = 2.91; natural sciences: M = 2.82; teachers: M = 2.76). There were no gender

by study major interactions (both goals: F < 1; occupational self-efficacy: F(6, 1205) = 1.55,

ns).

4.2 Descriptive Findings

Descriptive statistics and intercorrelations of the measures are presented in Table 1. At

time 1 there were small, but positive correlations between occupational self-efficacy and

mastery goals (r = .28), occupational self-efficacy and prestige goals (r = .19), and between

mastery goals and prestige goals (r = .21). The auto-correlations of occupational self-efficacy

at time 1 and time 3 (r = .51), mastery goals at time 1 and time 2 (r = .57), and prestige goals

at time 1 and time 2 (r = .70) were high, but different enough from 1 to indicate some malle-

ability of the constructs. All time 1 self-regulation variables were significantly related to objec-

tive career success and subjective career success at time 2. All self-regulation variables at

time 1, time 2, and time 3 were significantly related to objective career success and subjec-

tive career success at time 3. The autocorrelation of objective career success at time 2 and


objective career success at time 3 was high (r = .60). The correlation between time 2 and

time 3 subjective career success was of medium size (r = .38). The correlations between

objective career success and subjective career success were significant, but not very high

(maximum r = .31, minimum r = .13). Participants endorsed mastery goals more (time 1 M =

3.75; time 2 M = 4.06) than prestige goals (time 1 M = 3.02; time 2 M = 3.12), both t (1218) >

29.92, p < .001. Except from time 1 prestige goals all correlations of our self-regulation vari-

ables with gender were significant, but of small size.

4.3 Hypotheses Testing

We tested our hypotheses by means of structural equation modeling (SEM) using Mplus

(Muthén & Muthén, 2004). Structural equation modeling has several advantages. The meas-

urement model of the predictors can be included; measurement errors can be taken into ac-

count; the specific postulated paths can be tested; and besides providing the path coeffi-

cients, a series of overall fit statistics can be reported that show how well the empirical data

fits the theoretical model (Kline, 2005). We tested all models using Maximum Likelihood es-

timation with robust standard errors (MLR). All chi square difference tests were adjusted us-

ing a procedure adequate for the Satorra-Bentler chi square test statistic (Satorra, 2000). We

first modeled true change scores (TCM; Steyer, Eid & Schwenkmezger, 1997) for the re-

peated measures of mastery goals, prestige goals, and occupational self-efficacy. Then, we

tested the models on objective career success and on subjective career success, and we

also tested the models including gender.

4.3.1

Modeling of true change scores. Hypotheses (7) and (8) were concerned with changes

in the predictors. We decided not to compute simple difference scores because these suffer

from impoverished reliability. We rather modelled the change scores. Latent growth model-

ling is one possibility (LGM; Duncan, Duncan & Strycker, 2006; Singer & Willet, 2003). How-

ever, we decided not to use this approach because our design was comprised of only two

points of measurement for changes, and LGM is better suited when there are three points of

measurement (Singer & Willet, 2003). 4 Instead, we applied true change modelling (Steyer et


al., 1997). In TCM the observed indicators of the latent variable at the second time of meas-

urement are regressed on the latent variable at the first time of measurement. These re-

gressed indicators are treated as measures of the latent variable at time 2. The change vari-

able computed by this procedure is assumed to be free of measurement errors and it is al-

lowed to correlate and to be regressed like any other latent variable. This change variable

does not reflect the individual shape of a growth process, but rather reflects true interindi-

vidual differences in intraindividual change.

Following recommendations in the structural equation modeling literature (Dwyer, 1983;

Roberts, 1997) our latent variables were measured by three indicators each (the 6 items

measuring occupational self-efficacy both at time 1 and time 3 were converted into 3 parcels;

for the 4-item measures of mastery goals and prestige goals we used two items and one

parcel computed of the remaining two items). Measurement errors were allowed to correlate

over time for the same indicators of the same latent variable (Kline, 2005; Steyer et al.,

1997). 5

Figure 2 shows the resulting measurement model including the true change variables

(correlations between the latent constructs are not displayed). It has good fit statistics (χ²/df =

2.10, CFI = .98, TLI = .97, RMSEA = .030), despite a significant chi-square, χ²(1219) =

220.35, p < .001, which is due to the large sample size. All factor loadings are highly signifi-

cant. 6

4.3.2 Prediction of objective career success

Hypotheses (1), (2), and (3) had postulated influences of the self-regulation variables on

objective career success and Hypothesis (7) had assumed a reciprocal influence of objective

career success on the self-regulation variables. Our empirical data refers to three times of

measurement with time 1 measuring the self-regulation variables (occupational self-efficacy,

mastery goals, prestige goals), time 2 measuring objective career success and subjective

career success as well as again both goals, and time 3 measuring objective career success,

subjective career success and again occupational self-efficacy. We could test Hypotheses

(1), (2), and (3) both with respect to time 2 objective career success and time 3 objective


career success. And we could test Hypothesis (7) with respect to influences of time 2 objec-

tive career success on both goals and time 3 objective career success influences on occupa-

tional self-efficacy. 7

The model we tested was built of 6 latent variables (occupational self-efficacy, mastery

goals, prestige goals; and their true change scores) and two observed variables (objective

career success at time 2, objective career success at time 3). We treated objective career

success as an observed variable because objective career success was built from factual

information that is more or less free of measurement error (Boudreau, Boswell & Judge,

2001; Podsakoff & Organ, 1986); and because we had already weighted the components of

objective career success (see above) and therefore a simple measurement model was not

appropriate. The model allowed correlations between occupational self-efficacy, mastery

goals, and prestige goals at time 1, a correlation of the true change scores in mastery goals

and prestige goals, paths from every predictor at time 1 to time 2 and time 3 objective career

success, paths from true change scores in both goals at time 2 to objective career success at

time 3, paths from time 2 objective career success to the goals’ true change scores, and

paths from time 2 objective career success and time 3 objective career success to the true

change score of occupational self-efficacy. We also included a path from time 2 objective

career success to time 3 objective career success, which means that this autoregressive

model predicts change in time 3 objective career success. We also included a path from oc-

cupational self-efficacy at time 1 to the true change scores of mastery goals and prestige

goals, and we tested the paths from the true change scores in mastery goals and prestige

goals at time 2 to the true change score of occupational self-efficacy at time 3. There were

21 paths and correlations in this structural model. 14 were significant and a maximum of 28

paths and correlations could have been postulated. 89 parameters had to be estimated in the

total model. Due to our large sample size we were well in line with the recommendation of 10

to 15 persons per estimated parameter (Kline, 2005).

Figure 3 displays the resulting structural equation model. For the sake of clarity we ex-

cluded time 1 correlations and we also excluded non-significant paths. The model had good


fit statistics (χ²/df = 2.51, CFI = .97, TLI = .95, RMSEA = .037), despite a significant chi-

square, χ²(1219) = 334.28, p < .001. We computed Hoelter’s Critical N (Hoelter, 1983). Given

that its value is smaller than our sample size, a significant chi square does not matter. In our

case Hoelter’s Critical N was 300 and, hence, was much smaller than our sample size. The

R² values show that 7% of variance in objective career success at time 2, and 10% of vari-

ance in change of objective career success at time 3 was explained by this model.

Supporting Hypothesis (1) occupational self-efficacy at time 1 influenced objective ca-

reer success at time 2 (β = .09, p < .05) and objective career success change at time 3 (β =

.11, p < .01). Supporting Hypothesis (2) mastery goals at time 1 influenced objective career

success at time 2 (β = .13, p < .01). However, there was no influence of mastery goals at

time 1 on changes in objective career success at time 3 (β = .00, ns), and also no influence

of changes in mastery goals at time 2 to changes in objective career success at time 3 (β =

.10, ns). Supporting Hypothesis (3) prestige goals at time 1 influenced objective career suc-

cess at time 2 (β = .12, p < .001) and changes in objective career success at time 3 (β = .11,

p < .001). In accordance with Hypothesis (3) changes in prestige goals at time 2 influenced

changes in objective career success at time 3 (β = .15, p < .05). Objective career success at

time 2 influenced objective career success at time 3 (β = .55, p


are effects built from at least two paths (for instance, prestige goals via time 2 objective ca-

reer success to the true change score of prestige goals), and they are computed by multiply-

ing the β-values of the single paths. The coefficient is smaller the more paths are involved.

Hence, the size of specific indirect paths can only be compared if the number of paths is the

same. It is possible that certain specific indirect effects are significant whereas the overall

indirect effect is not.

Prestige goals had the comparatively strongest total (.18) and direct effect (.11) on time

3 changes in objective career success. Two specific indirect effects involving prestige goals

were also significant. One was the path from prestige goals at time 1 via objective career

success at time 2, the other one was the path from time 1 prestige goals via time 2 objective

career success and via change in prestige goals. Occupational self-efficacy had a total effect

of .14 (direct effect .13) with one significant specific indirect effect of occupational self-

efficacy via time 2 objective career success. Mastery goals had no direct effect on change in

time 3 objective career success, but one indirect effect via time 2 objective career success.

4.3.3 Prediction of subjective career success

Hypotheses (4), (5) and (6) had stated that the self-regulation variables influence sub-

jective career success, and Hypothesis (8) had assumed that subjective career success has

a reciprocal influence on these self-regulation variables. We tested these hypotheses with

the structural model as described above, and replaced time 2 and time 3 objective success

by time 2 and time 3 subjective career success. Due to the path from time 2 subjective ca-

reer success to time 3 subjective career success it was again an autoregressive change

model for time 3 subjective career success.

The model depicted in Figure 4 had good fit indices (χ²/df = 1.95, CFI = .98, TLI = .97,

RMSEA = .030) despite a significant chi square, χ²(1060) = 259.60, p < .001. Hoelter’s Criti-

cal N was 383 in this case, again much lower than our sample size. This means that the

model can be interpreted despite the significant chi square. The explained variance was 10%

for subjective career success at time 2 and 11% for the change in subjective career success

at time 3. In accord with Hypothesis (4) occupational self-efficacy influenced subjective ca-


reer success at time 2 (β = .23, p < .001) and subjective career success change at time 3 (β

= .21, p < .001). Supporting Hypothesis (6) the path from prestige goals to subjective career

success at time 2 was also significant (β = .12, p < .05). In contrast to Hypothesis (5) there

were no path from mastery goals to time 2 subjective career success (β = .07, ns). And there

were also no further significant paths from goals to subjective career success (mastery goals

to time 3 subjective career success change, β = .02, ns; true change score mastery goals to

time 3 subjective career success change, β = -.05, ns; prestige goals to time 3 subjective

career success change, β = .04, ns; true change score prestige goals to time 3 subjective

career success change, β = .09, ns). Finally, subjective career success at time 2 influenced

subjective career success at time 3 (β = .30, p < .001).

We again computed total, direct, and indirect effects of our variables on time 3 subjec-

tive career success change. Table 3 (upper panel) shows that occupational self-efficacy had

the largest total (.28), direct (.21), and indirect (.07) effect. One specific indirect effect from

occupational self-efficacy via subjective career success at time 2 was significant. Further-

more, there was one specific indirect effect of prestige goals via subjective career success at

time 2.

4.3.4 Reciprocal influences of objective and subjective career success on the self-regulation

variables.

In order to test Hypotheses (7) and (8) on reciprocal influences of objective and sub-

jective career success on changes in the self-regulation variables we tested a model in which

both objective career success and subjective career success were included. We tested paths

from time 2 objective career success and subjective career success on the changes in goals

and occupational self-efficacy, paths from time 3 changes in objective career success and

subjective career success on changes in occupational self-efficacy, and paths from changes

in time 2 goals on changes in time 3 occupational self-efficacy. The analysis revealed that

changes in mastery goals and prestige goals were influenced by both objective and subjec-

tive career success (mastery goals: time 2 objective career success β = .18, p < .01, time 2

subjective career success β = .21, p < .001, R² = .13; prestige goals: time 2 objective career


success β = .18, p < .001, time 2 subjective career success β = .18, p < .01, R² = .09). The

change in occupational self-efficacy, however, was only influenced by subjective career suc-

cess change at time 3 and change in mastery goals (β = .21, β = .22, p < .01, R² = .10). Time

2 objective career success (β = -.06, ns), time 2 subjective career success (β = .04, ns), and

time 3 objective career success change (β = .05, ns) had no influence.

4.4 Model Testing Including Gender

In order to test Hypotheses (9) and (10) we included gender as an observed variable in

the above models on objective career success and subjective career success. We added

paths from gender to time 1 self-regulation variables, to time 2 and time 3 objective career

success or subjective career success, and to the true change scores.

4.4.1 Objective career success

The structural equation model showed a good model fit (χ²/df = 2.83, CFI = .96, TLI =

.94, RMSEA = .039) despite a significant chi-square, χ²(1219) = 410.90, p < .001. 8 In accord

with Hypothesis (9) gender influenced time 2 objective career success (β = .20, p < .001) and

time 3 objective career success change (β = .15, p < .001) with lower success of women than

of men. Gender explained an additional 4% of variance in time 2 objective career success

(R² total = .11) and an additional 5% of variance in time 3 objective career success change

(R² total = .15).

Two paths from gender to time 1 self-regulation variables were significant, the path to

occupational self-efficacy (β = .08, p < .05; women had lower scores) and the path to mas-

tery goals (β = .13, p < .001; women had lower scores). Men showed more changes in pres-

tige goals (β = .10, p < .05) and in occupational self-efficacy (β = .13, p < .01) than women.

Gender had a highly significant total effect (.28) on time 3 changes in objective career suc-

cess (direct effect .15; indirect effect .13, see Table 2, lower panel). The specific indirect ef-

fects included time 1 occupational self-efficacy, time 2 objective career success, and a path

from gender via time 2 objective career success and via changes in prestige goals. Support-

ing Hypothesis (10) these findings suggest that the effects of gender on changes in time 3

objective career success were both direct and mediated. To further test this mediation we


compared the complete model (direct and mediated effects) with two models in which we

dropped one significant path at a time. Dropping the path from gender to occupational self-

efficacy resulted in a significant chi square difference, ∆ χ²(1) = 6.12, p < .02; and dropping

the path from changes in prestige goals to time 3 changes in objective career success also

resulted in a significant chi square difference, ∆ χ²(1) = 9.89, p < .005, i.e. the model includ-

ing both direct and mediated gender effects was better than the models in which mediated

effects were not allowed. 9

4.4.2 Subjective career success

The model had good fit indices (χ²/df = 2.31, CFI = .97, TLI = .95, RMSEA = .033) de-

spite a significant chi-square, χ²(1060) = 335.19, p < .001. Supporting a shifting standards

interpretation gender neither predicted subjective career success at time 2 (β = .05, ns) nor

subjective career success change at time 3 (β = -.01, ns). There were only two indirect ef-

fects of gender (see Table 3, lower panel), one involving the effect of gender via time 1 oc-

cupational self-efficacy, and the second one involving the effect of gender via time 1 occupa-

tional self-efficacy and via time 2 subjective career success. One additional percent of vari-

ance in both time 2 subjective career success and time 3 subjective career success change

was explained by gender, but the paths contributing to this increase were not significant.

4.5 The Longitudinal Relationship of Objective and Subjective Career Success

Finally, we analyzed the direction of the relationship between objective and subjective

career success. If they are merely correlated then the longitudinal influence of objective ca-

reer success at time 2 on subjective career success at time 3 should be completely mediated

by subjective career success at time 2 and the longitudinal influence of subjective career

success at time 2 on objective career success at time 3 should be completely mediated by

objective career success at time 2. However, if the relationship is directional, then there

should be no mediation. We conducted analyses of mediation (Baron & Kenny, 1986). The

prerequisite for such an analysis are significant correlations between all considered variables

(see Table 1). Then, one tests whether there is a drop in prediction of the criterion by the

respective predictor, when the possible mediator is controlled for. Mediation is given, if this


drop is significant. We found that the effect of time 2 objective career success on time 3 sub-

jective career success was completely mediated by time 2 subjective career success, indi-

cated by a significant drop in prediction (from β = .13, p < .001; to β = .01, ns), z = 7.99, p <

.001. The same result emerged for the prediction of time 3 objective career success via time

2 subjective career success: It was completely mediated by time 2 objective career success

as indicated by a significant drop in prediction (from β = .20, p < .001; to β = .03, ns), z =

9.82, p < .001. Conversely, the prediction of time 3 subjective career success via time 2 sub-

jective career success was not mediated by time 2 objective career success (from β = .38, p

< .001; to β = .37, p < .001), z < 1, and the prediction of time 3 objective career success by

time 2 objective career success was also not mediated by time 2 subjective career success

(from β = .57, p < .001; to β = .56, p < .001), z < 1. These findings suggest that subjective

career success and objective career success are correlated but—at least at this stage of

career development—one does not predict the other.

5. Discussion

The present research builds on the idea that in time of changing labor markets and of

changing career patterns there is a strong need for individuals to actively self-regulate their

careers. We wanted to test whether self-regulation, which largely happens in our heads, has

an influence on career success, and whether people have the power to actively control their

careers by purposeful thought. We considered two important components of self-regulatory

thought, namely self-efficacy beliefs and career-related goals. The latter were divided into

more “intrinsic” mastery-, and intellectual stimulation related goals versus more “extrinsic”,

materialistic and prestige related goals. We put forward the general assumption that indi-

viduals with high occupational self-efficacy beliefs and with both high mastery goals and high

prestige goals would be more successful than individuals with lower values in these self-

regulatory variables. We further asked whether such influences are restricted to subjective

measures of career success, such as satisfaction or comparative evaluation, or whether they

also have an influence on objective career success, such as pay or responsibility status.


Considering the cyclical nature of self-regulation we finally asked whether success—

subjective and/or objective—leads to changes in self-regulation variables.

5.1 Self-Regulation Influences Career Success

The data supported our general assumption: Individuals’ self-regulatory thoughts have

an influence on their career progression in a time span as long as three years. Both career-

related goals and occupational self-efficacy add to the prediction of subjective success (10%

explained variance at time 2, 11% explained variance of change in subjective success at

time 3) and also to the prediction of objective success (7% explained variance at time 2, and

10% explained variance of change in objective career success at time 3). We controlled for

our participants’ performance level by considering their GPA. Therefore, the present data

show that self-regulatory thoughts influence career progression above and beyond cognitive

ability as assessed by GPA.

Regarding objective success, materialistic, prestige-related goals had the highest influ-

ence. Supporting Hypothesis (3), people who just after graduation were oriented at climbing

up the career ladder, at having high responsibility and recognition, and at earning a lot of

money did, in fact, have more responsibility and recognition and did earn more money three

years later than people who pursued this goal to a lower degree. Moreover, we could show

that not only the initial level of prestige goals influenced objective career success, but that an

enhancement of prestige goals at time 2 had an influence on subsequent objective career

success as well. These findings support the theoretical notion of a feed-forward mechanism

in which goals lead to activities for attaining them and goal-attainment leads to adapted goal-

setting which instigates further efforts to attain these adapted goals (Bandura, 1986, 2001;

Bandura & Locke, 2003).

Supporting Hypothesis (1), occupational self-efficacy also had a significant influence on

objective career success. People who believed in their skills and motivation to perform the

occupational tasks they are confronted with were more successful than people with lower

self-efficacy beliefs. To our knowledge this is the first time that such a long-time influence of

self-efficacy on objective parameters of success has been demonstrated. The data also vali-


date cross-sectional findings on the relationship between self-efficacy, pay, and hierarchical

position (Day & Allen, 2004; Lubbers, et al., 2005). Due to the present design, in which the

second assessment of occupational self-efficacy took place at time 3, we could not test a

feed-forward influence of changes in occupational self-efficacy on later career success.

Therefore, we also could not test whether there is a negative influence of self-efficacy on

success if measured on an intra-individual level (see Vancouver & Kendall, 2006; Yeo &

Neal, 2006). 10

There was also an influence of mastery goals on objective career success. Supporting

Hypothesis (2), people who wanted to deal with challenging tasks and who wanted to im-

prove their competencies and skills were objectively more successful at time 2. Mediated via

time 2 objective career success, time 1 mastery goals also had an influence on time 3 objec-

tive career success. However, there was no feed-forward mechanism of changes in mastery

goals on changes in time 3 objective career success.

The finding that both mastery goals and prestige-related goals are important determi-

nants of objective career success—at least in the early career phase— fits with the data of

Frieze et al. (2006) and it fits with meta-analytical results by Payne et al. (2007; see also

Harachiewicz, et al., 1997; Barron & Harakiewicz, 2001), who reported that job performance

is positively related to both a learning goal orientation, which is more “intrinsic”, and a prove

goal orientation, which is more “extrinsic”.

Regarding subjective success, occupational self-efficacy was most important. People

with high self-efficacy beliefs felt more successful compared to their former fellow students

than people with lower occupational self-efficacy, thus supporting Hypothesis (4). This result

is in accord with meta-analytical findings by Dette et al. (2004) and Ng et al. (2005), who

showed that individual difference variables are very important for subjective career success.

It also validates cross-sectional data on self-efficacy and subjective career success (Judge &

Bono, 2001; see also Saks, 1995). Prestige goals had an influence as well (supporting Hy-

pothesis 6), but there was no feed-forward mechanism from changes in prestige goals to


changes in subjective career success. Contrary to Hypothesis (5), mastery goals had no in-

fluence on subjective career success.

All in all, the influence of mastery goals on career success was smaller than the influ-

ence of prestige goals and of occupational self-efficacy. This finding might be astonishing

given the large body of research showing that “intrinsic” goals are of utmost importance for

performance and optimal motivation (e.g., Dweck, 1986; Locke & Latham, 2002; Vande-

Walle, 1997; VandeWalle et al., 1999). However, one way to interpret this stronger influence

of prestige goals than of mastery goals on objective career success could be the closer con-

nection between the present operationalizations of prestige goals (among others: “I want to

make a lot of money”) and objective career success (among others: pay). Furthermore, the

finding that mastery goals are more important in the early phase of a career (influence on

time 2 objective career success) than in a later one (no influence on time 3 objective career

success) makes sense because the respective goals should be most important in times of

change, i.e., at the beginning of an employment or in case of change. We can only speculate

why there was no influence of mastery goals on subjective success. One possibility is that

the high mean and low variance in these goals (see Table 1) obscured a correlation. It is also

conceivable that people with high mastery goals are more reluctant to evaluate their career

as comparatively successful because their standards are very high.

5.2 Career Success Influences Self-Regulation

We empirically demonstrated the cyclical nature of self-regulation as postulated in self-

regulation theories (Bandura, 1986, 2001; Vancouver, 2000; Vancouver & Day, 2005; Zim-

merman, 2000). More successful individuals increased their mastery and prestige-related

goals more, and became more self-efficient than less successful people. Furthermore, we

could demonstrate that both objective and subjective success were important for these

changes. This means that people have to be objectively successful and have to interpret

their outcomes as success in order to initiate feed-forward processes of changes in goals.

Supporting Hypotheses (7) and (8) participants who were successful in their careers en-

hanced both their mastery goals and their prestige goals. Changes in self-efficacy, however,


were only induced by subjective success. Whereas experimental studies have demonstrated

the malleability of self-efficacy upon success or failure (Cervone et al., 2004; Gernigon &

Delloye, 2003; Smith et al., 2006) the present research found no impact of objective career

success on changes in occupational self-efficacy. Interestingly, changes in occupational self-

efficacy were also influenced by changes in mastery goals, i.e., when people raised their

mastery goals they also raised their occupational self-efficacy. Whereas there are already

findings showing that self-efficacy and mastery goals are correlated (see Payne et al., 2007;

Utman, 1997; Seijts, et al., 2004; Wood, Mento & Locke, 1987) the present results suggest

that there is a reciprocal relationship between these constructs. Increases in one of them

lead to increases in the other one as well.

5.3 Gender

In accord with previous research (Abele, 2003; Greene & DeBacker, 2004; Kirchmeyer,

1998; Ng et al., 2005) and supporting Hypothesis (9), women’s objective success was lower

than men’s. Including gender into the model of objective career success increased the ex-

plained variance by 4% in case of time 2 objective career success, and 5% in case of time 3

changes in objective career success. A detailed analysis of the reasons for this difference is

beyond the scope of the present article. However, supporting Hypothesis (10), the lower time

3 objective career success of women was in part mediated by their somewhat lower occupa-

tional self-efficacy and their smaller increase in prestige-related goals, which, in turn, was

influenced by women’s lower time 2 objective career success. We also found that women

showed less increase in occupational self-efficacy than men. As a possible conclusion from

these findings one can speculate that women’s lower objective career success will evoke

lower adaptations of career-related goals and perhaps also lower adaptations of occupational

self-efficacy, which will then lead to lower further career-success, and so on. This means

that–other things being equal–the gap in objective success between men and women should

increase over time. Subjective success did not differ between men and women. This finding

fits with the predictions of the shifting standards model (Biernat & Billings, 2001), according


to which the use of subjective scales leads to reduced differences between groups due to the

application of different anchors.

5.4 Career Success

Analyzing career success requires a clear conceptual understanding of this complex

construct. We differentiated between objective success and other-referent subjective suc-

cess. The moderate correlations between objective career success and subjective career

success that we found in our research are comparable to meta-analytical findings (Dette et

al., 2004; Ng et al., 2005). They suggest that both constructs tap into different domains. The

mediation analysis also showed that subjective career success cannot be influenced by ob-

jective career success and vice versa. Hence, it is a correlation and not a directed relation-

ship. Furthermore, the partly different results regarding the determinants of objective versus

subjective career success clearly indicate that it is important to distinguish the two.

5.5 Research Implications

The present results are important extensions of prior work. Regarding self-efficacy they

show that findings on the positive effect of self-efficacy on task performance and job per-

formance (Judge & Bono, 2001; Judge, et al., 2007; Judge et al., 1999; Lubbers et al., 2005;

Riggs et al., 1994; Stajkovic & Luthans, 1998) can be extended to a complex construct like

career success. Regarding goals, our findings suggest that the content of goals matters and

that the distinction between more “intrinsic” mastery-related goals and more “extrinsic” pres-

tige-related goals is promising. It is a core distinction inherent in many conceptualizations of

goals and motivation (cf. Austin & Vancouver, 1996; Dweck, 1986; Furnham, et al., 2005;

Hazer & Alvares, 1981; Locke & Latham, 2002; Nicholls, 1984; Super, 1957; VandeWalle,

1997; VandeWalle, et al., 1999, Zytowsky, 1994). Regarding objective and subjective career

success in a long-time perspective, our findings clearly suggest that both goals are important

and that mastery goals alone will not be sufficient (see also Harackiewisz, et al., 1997; Bar-

ron & Harackiewisz, 2001).

Further research will have to address how the influence of prestige goals on objective

career success is mediated. Previous research has shown that the effect of learning goals on


job performance is mediated via effort and planning (VandeWalle et al., 1999). In a similar

vein, individuals with high prestige goals possibly spend more time for their work, engage in

more career-planning, develop more specific competencies, and are more committed to their

careers. It is also conceivable that people with high prestige goals interpret their job experi-

ences in a more optimistic way as is suggested by the influence of prestige goals on subjec-

tive career success. More optimistic interpretations, in turn, could also lead to higher effort

and a stronger commitment.

The present research also has limitations which open perspectives for further investiga-

tion. First, future research should consider more individual difference variables and more

process variables possibly mediating the influences of self-efficacy and goals. As a recent

meta-analysis demonstrated self-efficacy effects might decrease if more individual difference

variables are taken into account (e.g., Judge, et al., 2007). Second, all our participants held a

university degree. Future research should test whether the present findings can be general-

ized for people with lower “human capital” (Ng. et al., 2005) in terms of education. Third, the

present research was only concerned with the first three years of participants’ careers and

should be expanded to later phases of occupational career development. Fourth, our opera-

tionalization of subjective career success was an other-referent comparative judgment. This

is advantageous, since such an operationalization is only rarely applied (Heslin, 2003). How-

ever, further research should also include self-referent subjective career success, for in-

stance career satisfaction, in order to capture the full meaning of subjective success (see

Dette et al., 2004; Heslin, 2003).

5.6 Applied Implications

Knowing more about malleable individual differences and of “purposeful thought” (Ban-

dura, 1997) that influence career success is of utmost importance for career planning and

career counseling. The present findings show that occupational self-efficacy, mastery goals,

and prestige goals are good “candidates” for self-regulatory career planning. It is not new

that self-efficacy beliefs may help in attaining goals (Eden & Aviram, 1993), and it is also not

new that goal-setting procedures and goal orientations are important for performance (cf.


Locke & Latham, 1990; Payne et al., 2007; Utman, 1997; Wood et al., 1987). However, it is

new that occupational self-efficacy, mastery goals, and prestige goals are important with re-

spect to complex and long-term measures of career success both on an objective and on a

subjective level. More “intrinsic” mastery goals are of utmost importance in every ambitious

occupational career. Besides this prerequisite, materialistic, “extrinsic” prestige-related goals

are even more important in attaining objective and subjective career success. Whereas psy-

chological theorizing sometimes treats extrinsic motivation and goals as less adaptive than

intrinsic ones (e.g. Deci & Ryan, 1985) the present findings do not support such a perspec-

tive.

Another important applied implication concerns the reciprocal influence of career suc-

cess on self-regulation. Success seems to initialize a feed-forward process of enhanced self-

regulation which, in turn, helps to attain more success. However, there may also be the con-

trary process in which low success does not enhance self-regulation which, in turn, does not

help to attain more success.

5.7 Conclusion

We sought to study the idea that self-regulation, that largely happens in our heads, has

an influence on factual (pay, position) and evaluative (subjective evaluation) career out-

comes. In accord with the general assumption that purposeful thought does have an influ-

ence on complex and long-reaching outcomes, we found that individuals with high occupa-

tional self-efficacy beliefs and with high both mastery goals and prestige goals were more

successful in their careers than individuals with lower values in these self-regulatory vari-

ables. We further found evidence for a reciprocal influence of career success on self-

regulation. Success led to feed-forward processes of goal adaptation which, in turn, influ-

enced further success. The findings are interesting with respect to theorizing on self-

regulation and career development, with respect to the conceptualization of goal content,

with respect to the complex and opalescent construct of occupational career success, and

with respect to applied perspectives of training and skills development.


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Documents

1. Introduction well. A number of new constructs have been ...Previous research revealed that the determinants of objective vs. subjective success Self-Regulation and Career Success