Understanding Career Decision Self-Efficacy

Abstract

This study used meta-analysis to investigate the relationships between career decision self-efficacy (CDSE) and its relevant variables. The authors aimed to integrate the mixed results reported by previous empirical studies and obtain a clearer understanding of CDSE’s role within the framework of social cognitive career theory (SCCT). For purposes of this study, the authors searched and selected nine relevant variables (gender, age, race, self-esteem, vocational identity, career barriers, peer support, vocational outcome expectation, and career indecision). While some variables (i.e., gender, race, and career barriers) did not have a significant effect on CDSE, in accordance with the SCCT model, CDSE correlated significantly to self-esteem, vocational identity, peer support, vocational outcome expectation, and career indecision variables. The authors discuss these results in the context of the SCCT.

Keywords

career decision self-efficacy meta-analysis social cognitive career theory

Career self-efficacy is one of the most vigorously studied factors in career literature. Social cognitive career theory (SCCT; Gushue, 2006; Lent, Brown, & Hackett, 1994, 1996; Lent, Sheu, et al., 2008; Nauta, 2004; Silvia, 2003), which expanded Bandura’s (1986) social cognitive theory to explain the dynamics of various internal and external career development factors, has recently highlighted career self-efficacy’s importance. SCCT proposes that a wide range of individual and distal contextual factors contributes to a person’s learning experiences that serve as a basis for developing self-efficacy and outcome expectations. These self-efficacy and outcome expectations, in turn, lead to the generation of interests, goals, and career development performance. Moreover, immediate contextual influences, such as social support and career barriers, can also affect self-efficacy’s influence on an individual’s interests, goals, and performance. SCCT has been a useful framework for helping researchers understand career self-efficacy’s role in career behaviors. Numerous studies using the SCCT framework have demonstrated that career self-efficacy plays a key role in an individual’s career planning and development (Gushue & Whitson, 2006; Lease, 2006; Lent et al., 2001; Lent et al., 2003; Lent et al., 2005).

Advances in career self-efficacy research have highlighted a need to differentiate self-efficacy for distinctive types of career tasks. Hackett and Betz (1981) proposed two unique domains of career self-efficacy: the content and process domains of career decision making. The content domain of career self-efficacy refers to self-efficacy in specific career fields, such as math, writing, or science; whereas the process domain of career self-efficacy centers on self-efficacy in using the necessary strategies for successfully navigating a decision-making process. These sophisticated demarcations of career self-efficacy into content and process domains are followed by the recognition that self-efficacy measures should be developed for specific career domains (Betz & Hackett, 2006). For example, the content-domain self-efficacy measures were developed to assess career self-efficacy for various college majors (Fouad, Smith, & Zao, 2002; Lent et al., 2001; Lent et al., 2003). Examples of process-domain self-efficacy measures are career search activities (Solberg et al., 1994) and career decision-making behaviors (Taylor & Betz, 1983).

In this study, we primarily focused on the Career Decision Self-Efficacy Scale (CDSES; Taylor & Betz, 1983), a measure of process-domain self-efficacy. We wished to distinguish the content-domain and process-domain self-efficacies, since they concern distinctive aspects of career decisions and relate to different sets of variables. Although content-domain self-efficacy also plays an important role in career decision making, we targeted only process-domain self-efficacy, as we were interested in how people make career decisions rather than in the types of careers people choose. In addition, our study centered on a specific measure of process-domain self-efficacy: the CDSES. Previous studies on process-domain self-efficacy mainly used the CDSES and the Career Search Self-Efficacy Scale (CSES; Solberg et al., 1994). While the CDSES addresses aspects of the global decision-making process (e.g., understanding self and occupations, goal setting, and planning), the CSES orients more toward specific job search activities (e.g., job searching, networking, and interviewing). Thus, we concluded that the CDSES is a more appropriate measure for the purpose of our study: examining the overall decision-making process.

The CDSES (Taylor & Betz, 1983) measures the degree to which individuals feel confident at completing tasks related to career decision making. The CDSES comprises five 10-item subscales: Self-Appraisal, Occupational Information, Goal Selection, Planning, and Problem Solving. The CDSES uses a 10-point Likert-type scale ranging from 0 (no confidence at all) to 9 (complete confidence), yielding a total score range of 0–450. Low scores indicate low self-efficacy for career decision making. Researchers have developed several short versions of the CDSES and modified some to fit certain groups, such as middle school students or high school students (e.g., Anderson & Brown, 1997; Betz, Klein, & Taylor, 1996; Carns et al., 1995). The Career Decision Self-Efficacy Scale–Short Form (CDSES-SF) is the most frequently used version, consisting of 25 items that represent the original five subscales and employing the original 10-point Likert-type scale (Betz et al., 1996).

CDSE has been recognized as an important factor, associated with diverse career-related behaviors. CDSE is a strong predictor of career indecision (Bergeron & Romano, 1994; Betz & Luzzo, 1996; Taylor & Betz, 1983). Furthermore, it correlates positively with career adjustment (Betz & Luzzo, 1996), career exploration behavior (Blustein, 1989), and other career decision-making attitudes and skills (Luzzo, 1995, 1996), thus demonstrating a link between adaptive career development and CDSE. Moreover, several psychological variables, such as internal locus of control (Taylor & Popma, 1990) and global self-esteem (Betz & Klein, 1996), have positive relationships with CDSE.

Although CDSE indicates positive career attitudes and behaviors, as well as psychological adjustment, results of previous studies investigating the relationship between CDSE and demographics (e.g., gender, race, and age) or career-related variables (e.g., vocational expectation and career barriers) have been mixed. For example, various studies on CDSE and demographic variables such as gender, race, and age have found significant correlations (Creed & Patton, 2003; Smith & Betz, 2002); whereas other studies have reported these demographic variables have no significant correlations with CDSE (Creed, Patton, & Watson, 2002; Hampton, 2006). Researchers hold varying points of view about the relationship between CDSE and career-related variables such as vocational expectation and career barriers (Betz, Hommond, & Multon, 2005; Brown, Shelton, & Dipoto, 1999; McWhirter, Rasheed, & Crothers, 2000; Wang, Jome, Haase, & Bruch, 2006). For example, some studies have found significant correlations between CDSE and career barriers (McWhirter et al., 2000), whereas others have reported no significant relationships between the two constructs (Brown, Reedy, Fountain, Johnson, & Dichiser, 2000; Patton & Creed, 2007).

Due to the mixed results of the studies on the relationship between CDSE and certain demographic (e.g., gender, race, and age) and career-related (e.g., vocational expectations and career barriers) variables, we perceived a need for the use of more comprehensive methods, such as meta-analysis, on this research issue. The meta-analysis method integrates the results of independent studies. Researchers perform meta-analyses when they have a sizable amount of data, sometimes contradictory, from many studies. Meta-analysis can be a useful tool for integrating such data. This method enabled us to synthesize the results from those studies that investigated various relevant variables related to CDSE (Fouad & Byars-Winston, 2005). Through meta-analysis, researchers can draw integrative results by analyzing various data sets.

Unfortunately, few studies have attempted to integrate the considerably diverse research findings on CDSE (Nilsson, Schmidt, & Meek, 2002). While previous, extensive studies on the relationships between CDSE and assorted career variables are valuable, the integration of these studies may offer a clearer view of CDSE’s dynamics. Thus, we investigated the relationship between CDSE and other relevant career variables within the framework of the SCCT using a meta-analytic approach. We hypothesized that CDSE’s relationships with contextual variables (e.g., career barriers) as well as career attitudes and behaviors (e.g., vocational identity, outcome expectation, and career indecision) are stronger than its relationship with demographic variables (e.g., race, age, and gender). While demographic variables are undeniably important in sculpting CDSE, their effects may be multifaceted and indistinct, as they influence CDSE indirectly by shaping learning opportunities and experiences (Lent et al., 1994; Lindley, 2006). On the other hand, CDSE ought to be more closely related to contextual variables, career attitudes, and behaviors, as they are in immediate relationship (Gushue, Scanlan, Pantzer, & Clarke, 2006; Lent et al., 1994; Lent et al., 2003; Lent, Lopez, Lopez, & Sheu, 2008). Such findings could help researchers to obtain a clearer understanding of CDSE’s roles in career decision making and facilitate designing career interventions for improving CDSE.

Method

Literature Search

To explore relevant variables' effects on CDSE, we investigated articles written in English, from 1983 to 2008 (a 25-year period), because the original CDSES first saw publication in 1983. Our computer search scanned keywords for possible combinations of “career” with “self-efficacy,” in databases such as PsychINFO, Science Direct, EBSCO, ERIC, and Google Scholar. We then manually searched the seven major career counseling and development journals: Journal of Career Development, Journal of Career Assessment, Career Development Quarterly, Journal of Vocational Behavior, Journal of Employment Counseling, Journal of Counseling and Development, and Journal of Counseling Psychology. We carried out this literature search from September to October 2008.

Selection Criteria

We applied the following criteria to select appropriate articles for the meta-analysis:

Year of publication

We selected articles published from 1983 to 2008.

CDSES

In the various studies dealing with career self efficacy, we excluded studies using content-domain self-efficacy measures, such as the Occupational Self-efficacy Scale (Osipow & Temple, 1996) and the Skills Confidence Inventory (SCI; Betz, Borgen, & Harmon, 1996), since content-domain self-efficacy is beyond the scope of this study. We included only the CDSES (Taylor & Betz, 1983) and the CDSES-SF (Betz et al., 1996), as they measure the process-domain of self-efficacy and they have been the most popular instruments among such studies.

Selection of relevant variables

Our analysis included studies investigating demographic variables (e.g., gender and age), psychological variables (e.g., self-esteem and peer support), and career-related variables (e.g., career indecision and career barriers) via the CDSES. To improve the meta-analysis's accuracy and generalizability, we included as relevant variables those that three or more empirical studies had examined.

Available data

We selected only studies examining quantitatively available data, for example, means, standard deviations, and correlations.

As a result, we identified nine variables meeting these criteria: demographic variables (gender, age, and race), psychological variables (self-esteem and peer support), and career-related variables (career indecision, vocational outcome expectation, career barriers, and vocational identity). Initially, we obtained 401 articles. We included articles (n = 84) containing statistical data such as means, standard deviations, t statistics, or correlation coefficients and excluded research (n = 36) that only showed multiple regression results. We only selected articles that included demographic variables (e.g., gender and age), psychological variables (e.g., self-esteem and peer support), and career-related variables (e.g., career indecision and career barriers). Based on these criteria, we analyzed 34 articles. Coding categories were total sample size, sample size for each variable, means, standard deviations, and statistics calculated (e.g., Pearson r correlation or t value).

Statistical Procedures

The included studies used a variety of statistical analyses, which provided diverse statistics (e.g., $\bar{x}$ , s, t values, and r). We converted all statistics to the correlation coefficient r, which produced a common effect size (Rosenthal, 1991). We converted t values with means and standard deviations to correlation coefficient r because studies with dichotomous variables, such as gender, provided two different types of statistics. Then, we transformed all r statistics into Fisher’s z, to create a normal distribution, and then weighted them by their degrees of freedom (0–3). Finally, we reconverted the weighted average of Fisher’s z to the weighted average of r, to provide a weighted average effect size for each variable (Charter & Larsen, 1983).

We tested heterogeneity because using the chi-square test with small samples or a low number of studies is inappropriate (Deeks, Higgins, & Altman, 2005). In addition, the selected studies collected quite a few nonsignificant data, especially regarding gender (significant, 2; nonsignificant, 11). However, heterogeneity does not always yield nonsignificant results, so we adjusted the p value level from .05 to .10, in accordance with the recommendation by Deeks et al. (2005). Thus, we used the I ² statistic to calculate the percentage of total variation across the studies that was due to heterogeneity rather than chance. The value of 0% for I ² indicates little chance of heterogeneity and a higher I ² value indicates a higher degree of heterogeneity. In this part of our study, we used a random effects model for measuring the weighted average effect size (r and Fisher’s z), because all the studies were heterogeneous, except for gender, age, and peer support, on each variable when we identified the significant probability of heterogeneity. The MIX (Meta-analysis with Interactive eXplanations) program was used to conduct the meta-analyses, using Visual Basic as a main language, based on Excel (Choi & Guck, 2008).

Results

We found 34 studies that met the literature selection criteria and included nine variables in the meta-analysis: gender, age, race, career indecision, vocational outcome expectation, self-esteem, career barriers, vocational identity, and peer support. Total sample size was 18,388 and the sample size for each article ranged from 61 to 925 (mean = 164). Some of the selected variables were measured by various scales. However, we identified each psychological and career-related variable based on an SCCT framework. Overall, we divided the nine relevant variables into five categorizations: Person Inputs (gender, age, and race), Self-Concept (self-esteem and vocational identity), Contextual Influences Proximal to Choice Behavior (career barriers and peer support), Outcome Expectations (vocational outcome expectation), and Goals (career indecision). Table 1 presents the selected variables and the scales used for each one. Of these five categories, the original SCCT model does not include the self-concept category. However, studies frequently examine the self-concept variables, self-esteem, and vocational identity, with the career self-efficacy variable. Thus, we added this category to provide for the possibility of expanding the SCCT model. In addition, we included the career indecision variable in the Goals category. The rationale was that individuals having lower scores on the career indecision variable would have discernible goals regarding their future vocations than would those who had higher scores (Betz & Voyten, 1997; Hughes & Karp, 2004).

Table 1.

Scales for Measuring Each Relevant Variable

Variables	No. of studies	Scales
1. Gender	14	Means and SD or t values (6 studies^a) Correlation coefficients (8 studies)	Measured statistics
2. Age	8	Means and SD or t values (6 studies) Correlation coefficients (2 studies)
3. Race	4	Minority vs. Caucasian (2 studies) Students of Color vs. White (2 studies)
4. Self-esteem	5	Self-Regard Scale (USRS; Betz, Wohlgemuth, Serling, Harshbarger, & Klein, 1995; 1 study) Rosenberg Self-esteem Scale (RSE; Rosenberg, 1965; 2 studies) Coopersmith Self-esteem Inventory (SEI; Coopersmith, 1987; 1 study) General Self-Efficacy Scale (GSE; Sherer et al., 1982; 1 study)
5. Vocational identity	6	My Vocational Situation (MVS; Holland, Daiger, & Power, 1980; 6 studies)
6. Career barriers	4	Perceptions of Educational Barriers (PEB; McWhirter, 1997; 1 study) Perceived Barriers Scale (PBS; Howell, Frese, & Sollie, 1977; 2 studies) Career Barriers Inventory-Revised (CBI-R; Swanson, Daniels, & Tokar, 1996; 1 study)
7. Peer support	4	Peer Control and Autonomy Support Scale (PSCASS; Pelletier, 1992; 1 study) Social Support Microsystems Scale (SSMS; Seidman et al., 1995; 1 study) Inventory of Parent and Peer Attachment (IPPA; Armsden & Greenberg, 1987; 2 studies)
8. Vocational outcome expectation	6	Vocational Outcome Expectations scale (VOE; McWhirter, Rasheed, & Crothers, 2000; 4 studies) Career Decision Outcome Expectations Scale (CDMOES; Betz & Voyten, 1997; 1 study) Personal strivings measure (Emmons, 1999; 1 study)
9. Career indecision	12	Career Decision Scale (CDS; Osipow, 1987; 10 studies) Career Decision Scale (CDS; Osipow, Carney, Winer, Yanico, & Koschier, 1980; 1 study) Career Decision Scale (CDS; Osipow, Carney, & Barak, 1976; 1 study)

Note. ^aNumber of studies that used the scale.

Estimates of Relevant Variables to CDSE

Table 2 shows the number of studies on each variable, sample sizes across studies, weighted average effect sizes (r), including the 95% confidential interval, average Fisher’s z for testing significance, probability of heterogeneity across studies, and percentage of total variance across studies (I ²). In addition, we calculated correlation coefficients (r _c) and corrected the average effect size (r) by measurement error, because the reliabilities of the variables influence the calculation of the correlation (Hunter & Schmidt, 1990). For this analysis, we computed each variables' mean reliability. The mean reliability of CDSE is .92; self-esteem is .86; vocational identity, .84; career barriers, .83; peer support, .79; outcome expectation, .71; and career indecision, .90. The difference between the uncorrected (r) and the corrected correlation coefficients (r _c) would generally be less than 0.09, with corrected values being higher than uncorrected ones. Because the differences between uncorrected and corrected correlations in this meta-analysis were relatively low, we did not do any comparisons between the uncorrected (r) and the corrected correlation coefficients. Among the variables used in the meta-analysis, the most studied variable was career indecision (number of studies = 14, N = 4,460), and the least was race (number of studies = 4, N = 713). Most of the variables occurred in at least four studies having more than 1,000 subjects or participants.

Table 2.

Meta-Analytical Summary of Relevant Variables to CDSE

Variables	No. of Studies	Sample Size	Weighted Average r	95% Confidential Interval	r _c ^a	Average z	Heterogeneity	I ²%
1. Gender	14	3,769	.00	[−.02, .04]	.00	.53	17.97	33.22
2. Age	8	1,724	.13	[.08, .18]	.14	5.42	13.19	46.93
3. Race	4	713	−.02	[−.31, .28]	−.02	.10	50.07	94.01
4. Self-esteem	5	1,154	.49	[.39, .59]	.55	9.87	13.20	69.71
5. Vocational identity	6	1,175	.48	[.38, .58]	.55	9.11	17.62	71.62
6. Career barriers	4	1,292	−.09	[−.30, .12]	−.10	.81	27.30	89.01
7. Peer support	4	1,223	.35	[.30, .41]	.41	13.21	6.00**	50.33
8. Vocational expectation	6	1,104	.40	[.30, .51]	.49	7.40	18.75	73.33
9. Career indecision	12	4,460	−.52	[−.55, −.48]	−.57	26.36	22.49	51.09

Note. CDSE = career decision self-efficacy.

^aCorrelation coefficient corrected for measurement error.

* p < .05.

** p < .01.

Regarding the magnitudes of the corrected correlation coefficients (r _c), the variables of the self-concept domain vocational identity (r _c = .55, p < .001) and self-esteem (r _c = .55, p < .001) showed the largest significant and positive effect on CDSE. In addition, age (r _c = .14, p < .001) showed a small but significant effect on CDSE. With regard to race, the equation for calculating t value from means and standard deviations yielded very small correlation coefficients (r _c = −.02). Using an identical procedure for race, we found gender’s effect size (r _c = .00) was also insignificant. Neither gender (r _c = .00, p = .59) nor race (r _c = −.02, p = .92) were significant predictors of CDSE. In the contextual influences domain, carrier barriers (r _c = −.10, p = .41) showed an insignificant effect on CDSE. On the other hand, peer support (r _c = .41, p < .001) significantly and positively predicted CDSE, with a moderate effect size. Also, vocational outcome expectation (r _c = .49, p < .001), as the domain of outcome expectations, appeared to have a significant, positive effect. In addition, career indecision (r _c = −.57, p < .001), as the variable in the domain of goals, showed a large negative effect, with a large sample size (n = 4,460).

In addition, the results of the weighted average effect size (r) were similar to the aspect of the corrected correlation coefficients (r _c). In accordance with the criteria, Cohen (1988) suggested about the effect size of Pearson’s r, age (r = .13) and career barriers (r = −.09) showed a small effect. Peer support (r = .35) and vocational outcome expectation (r = .40) had medium-size effects on CDSE. Self-esteem (r = .49), vocational identity (r = .48), and career indecision (r = −.52) had large, strong effects on CDSE.

Last, regarding effect size heterogeneity, almost all variables showed a significant probability of heterogeneity across studies except gender, age, and peer support. Heterogeneity is defined as the presence of variation in authentic effect sizes underlying dissimilar studies; thus, assessing heterogeneity is an important procedure in meta-analyses. There are a number of possible explanations for effect size heterogeneity. One explanation is related to sample size; if the number of studies is too low, the homogeneity tendency is reduced. Also, if there are moderating variables, the probability of heterogeneity appearing increases inversely. In our study’s results, the percentage of I ² on race indicated a high degree of heterogeneity (I ² = 94.01%). This means the results of the effect size regarding race differ considerably across studies and this result may be due to the small sample size or other moderating variables. The other variables also showed a high degree of heterogeneity. Therefore, future studies need to investigate the moderating variables and include a larger sample size.

Discussion

This study investigated the relationship between CDSE and demographic, career-related, and self-concept variables using a meta-analytic approach. The analysis included nine variables from 34 studies in seven major journals in the fields of counseling, career, and employment. The following presents our results in accordance with the SCCT framework. None of the demographical variables tested have a significant effect size on CDSE. This result implies that gender and race may not be a critical factor in CDSE, which was demonstrated by several previous studies (Creed et al., 2002; Creed, Patton, & Bartrum, 2004; Fouad et al., 2002; Hampton, 2006). However, the nonsignificant effects of gender and race in this study should be interpreted with caution, as they may not necessarily indicate that these variables are irrelevant CDSE factors. Rather, this result may imply that the relationships between gender and race and CDSE are indirect, mediated, or moderated by various learning experiences, as indicated by the SCCT model. The effect size for the relationship between CDSE and vocational outcome expectation was moderate confirming the SCCT proposition that career self-efficacy predicts career outcome expectations (Lent et al., 1994). Social cognitive theory proposes that the influence of self-efficacy on outcome expectations is greater for tasks where performance quality is crucial for achieving desired outcomes (Bandura, 1989). However, not just personal abilities but also by environmental conditions affect career development issues (Lent et al., 1994). Thus, this moderate effect size appears appropriate for the nature of the relationship between CDSE and career outcome expectations.

Peer support is another factor that correlated significantly with CDSE. The SCCT model proposes that peer support serves as one type of proximal contextual influence on CDSE. Though we included only a limited number of studies on peer support, it seems consistently associated with career self-efficacy, as indicated by its low I ² value (50.33%). However, career barriers, another proximal contextual influence variable in the SCCT model, did not have a significant effect on CDSE. Numerous previous studies also failed to find a significant relationship between career barriers and CDSE (Betz et al., 2005; Brown et al., 2000; McWhirter et al., 2000; Wang et al., 2006). This nonsignificant relationship indicates several possibilities. First, it may indicate that previous learning experience mediates the effects of career barriers. SCCT researchers have debated the direct or indirect effects of career barriers (see Lent et al., 2001). Thus, further examination of the role of career barriers is warranted. We find an alternative explanation in the heterogeneous measures used regarding career barriers. In SCCT, proximal contextual influences concern only external and immediate factors. However, our meta-analysis included three types of career barrier measurements. Although all represent the construct “career barriers,” they may measure different aspects of career barriers. Also, they may differ in topographic (internal vs. external) and temporal (immediate vs. distant) emphases, which could have contributed to the nonsignificant finding regarding career barriers and CDSE.

We found the largest effects on CDSE within the self-concept variables: self-esteem and vocational identity. Although the SCCT model does not articulate the role of self-concept in CDSE, our study showed that greater self-esteem and vocational identity closely correlated with higher CDSE. This result may indicate that CDSE is an important element of general (i.e., self-esteem) and career specific (i.e., vocational identity) self-concept. Previous findings reporting a strong relationship between them (Brown et al., 2000; Creed et al., 2004; Solberg, Good, Fischer, Brown, & Nord, 1995) support this idea. Finally, CDSE had a large negative effect on career indecision. This result supports numerous empirical studies consistently reporting a negative relationship between CDSE and career indecision (Bergeron & Romano, 1994; Betz & Luzzo, 1996; Taylor & Betz, 1983). As proposed by self-efficacy theory (e.g., Bandura, 1986), it appears self-efficacy for career decision making is a strong predictor of one’s level of career decision.

Thus, our results suggest that equivocal findings regarding the relationships between CDSE and certain demographic (e.g., gender and race) and career-related (e.g., career barriers) variables may be due to the indirect nature of these relationships. Our meta-analysis showed that CDSE has no significant direct relationship with gender, race, and career barriers and only a moderate relationship with vocational outcome expectation. As the SCCT model proposes, these variables' associations with CDSE appear mediated by various other factors, and they do not directly affect CDSE development.

Implications for Research and Practice

This study’s findings have several implications for researchers and practitioners specializing in career development and counseling and offer a comprehensive view of various factors associated with CDSE. CDSE plays a crucial role in diverse career behaviors, yet studies on it have reported rather inconsistent findings. This study identified general and career-specific factors that correlate more strongly and directly with CDSE, which may help future researchers design CDSE studies. In addition, we attempted to incorporate two self-concept factors (self-esteem and vocational identity), which are absent from the SCCT model, into our understanding of CDSE. While the SCCT model has been a valuable framework for career research and fostered career related self-efficacy studies (see Lent, 2005), it does not articulate the role of self-concept. The strong relationship this study found between self-concept factors and CDSE justifies SCCT researchers' attention to self-concept.

This study also has practical implications for career counselors. The strong relationship this study found between CDSE and career indecision validates interventions focusing on CDSE. Career indecision is a complicated phenomenon influenced by dynamic factors (Felsman & Blustein, 1999; Leong & Chervinko, 1996; Whiston, 1996). CDSE is possibly the most predictable factor, as our study indicates. Thus, focusing on improving CDSE may be one of the most efficacious strategies for addressing career indecision. This study also highlights the importance of social support. Career counselors may work with clients more effectively by helping them use networking and develop support systems. Finally, these findings suggest career counselors should also consider self-concept factors when assisting low-CDSE clients. CDSE is the self-appraisal of one’s capability regarding specific career decision tasks and counselors often design specific tasks and exercises relevant to career decision as part of an intervention (Sullivan & Mahalik, 2000). However, CDSE’s close relationship with self-concept factors implies that improving global self-concepts, such as self-esteem and vocational identity, can enhance it. Career counselors should design effective interventions for improving CDSE through an understanding of the entire self-concept configuration, with close attention to personal and developmental issues that may interfere with personal agency.

This study has several limitations. First, it only included CDSES (Taylor & Betz, 1983) and CDSES-SF (Betz et al., 1996), which measure individual self-efficacy in career decision making. We excluded other career-related self-efficacy measures, as self-efficacy in making career decisions is distinct from self-efficacy for other career behaviors (Betz & Hackett, 2006). Thus, this study’s findings may not be applicable to other types of self-efficacy measures. We consider that another meta-analysis study, on other career-related self-efficacies such as career search self-efficacy, is needed for comparison. In addition, the studies included in our meta-analysis were relatively limited, as our search considered only published studies in journal articles. Subsequent research examining additional studies, from thesis and dissertation databases, would allow a broader generalization of the meta-analytical results. In addition, because this study’s purpose was to investigate the relationships between CDSE and its relevant variables, we did not examine the relationships between outcome expectations and goals in the SCCT model. Finally, this study was unable to investigate all domains in the SCCT model, such as choice action and persistence, due to the dearth of studies concerning them. Future researchers will need an increased focus on the behavioral results of CDSE in their studies.

Despite these limitations, this study attempted to clarify the role of CDSE using the SCCT framework and identified social cognitive variables, including self-concept variables that are crucial for CDSE from an expanded framework. These findings may facilitate SCCT studies centered on process-specific career self-efficacy as well as serving as a rationale for designing career interventions for CDSE.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the authorship and/or publication of this article.

Funding

The author(s) received no financial support for the research and/or authorship of this article.

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