A test of the career self-management model with Turkish undergraduate students

Abstract

The purpose of this study was to test the career self-management model among Turkish undergraduate students. For this purpose, data were collected from a group of 428 Turkish undergraduate students attending a state university located in İstanbul province. A structural equation model was used to determine the variables that associated with career decidedness of university students. As a result, the conscientiousness personality trait was removed from the model. Additionally, career decision-making self-efficacy, vocational outcome expectations, and goal feedback exerted a significant and direct effect on university students’ career decidedness. Finally, the career self-management model was found to be partially confirmed.

Keywords

Career self-management model career decidedness non-Western society

The university period is a stage when individuals prepare for the transition to working life and make important decisions related to careers. In this period, on one hand students attempt to fulfill all the requirements of the major they attend, and on the other must make certain decisions determining their career future. Changes experienced at present in technological, economic, health, and cultural fields significantly affect the working world and continue to create uncertainties about career developments for university students.

With all these changes, this period is stated to cause a world of volatility, uncertainty, complexity and ambiguity (VUCA) and career directions are accepted as being more unstable (U.S. Army Heritage and Education Center, 2019). In this century with these features, it is important to cultivate individuals who can solve their own problems, are flexible in coping with the VUCA perspective, continuously develop knowledge and competency, are open to change, can find their own field, and respond to the complexity of the world (Di Fabio & Gori, 2016; Savickas, 2011). For this reason, behavior ensuring the ability to adjust to changes occurring in career development becomes important.

Behaviors ensuring adjustment offer routes allowing students to follow their career goals. From this aspect, adaptive career behaviors are not a final outcome, but represent a decision journey which may result in outcomes like career decidedness and employment (Lent et al., 2019). Stated differently, the focus should be on adaptive career behaviors and the importance of context in this period perceived as being more fluid and more difficult to predict (Bauman, 2005). The career self-management model (CSM; Lent & Brown, 2013) emphasizing the importance of focusing on context and process and targeting identification of processes which will assist people in directing their own careers and educational behaviors in a variety of contexts, deals comprehensively with adaptive behaviors and the variables affecting them.

SCCT's CSM model

CSM was developed by Lent and Brown (2013) based on the social cognitive career theory (SCCT). This model focuses on the career development tasks of individuals, and how they manage unpredictable events and crises. Contrary to previous social cognitive career models, CSM focuses on the process dimension of career behaviors. The previous SCCT models comprise three interconnected models describing the development of interests, career choice, and performance dimensions, and focusing on the content. Additionally, Lent and Brown (2006; 2008) added a fourth model involving satisfaction/well-being in the educational and professional fields. The fifth model of CSM focuses on a broader career adaptation case throughout life and aims to complement previous models rather than change the four available SCCT models (Lent & Brown, 2013). Behaviors like exploring career choices, making a decision between these choices, searching for work and managing a variety of work transitions are behaviors which may display development during the process. Used by individuals with the aim of achieving their career targets, these behaviors also target coping with a variety of changes occurring in the working world so the CSM model can be qualified as adaptive career behavior (Lent & Brown, 2013).

According to CSM, there are a variety of predictors for adaptive career behaviors. Among these predictors are cognitive-person factors (self-efficacy, outcome expectations, and goals), contextual influences (such as career barriers), and personality traits (such as conscientiousness) (Lent et al., 2017). Self-efficacy, outcome expectations, personality traits, and career barriers directly or indirectly undertake roles encouraging adaptive career behaviors (e.g., career decidedness) (Lent & Brown, 2013). In this sense, career self-management can be understood as a dynamic action regulation process at the intersection of work and non-work life roles, affected by personal and contextual role expectations and resources and barriers (Hirschi et al., 2022). Moving from this point, in this study the target was to investigate the relationship between conscientiousness, career decision-making self-efficacy (CDMSE), outcome expectations, career barriers, and career goal feedback variables with career decidedness. In this way, the target was to test the major portion of the CSM model.

Career decidedness

Career decidedness represents the level of confidence and certainty individuals feel about their ability to achieve goals related to their future career (Gordon, 1998; Restubog et al., 2010). In other words, career decidedness means the opposite of indecision experienced related to career goals. Today, when career self-management is considered as a dynamic self-regulation process, career decidedness becomes a critical variable for individuals to set effective career goals. As a matter of fact, career decidedness appears as an important outcome variable in studies based on CSM (Hirschi & Koen, 2021). Therefore, when students in the university period are examined in terms of this development period, exploratory of career choices, determination of possible career goals and deciding between these goals may be included among adaptive career behaviors.

Cognitive-person factors

In the CSM, self-efficacy and outcome expectations are considered to be the basic motivational elements making the goal-action-outcome process possible (determining career goals, taking action in line with these goals and achieving outcomes) (Lent & Brown, 2013). In this model, self-efficacy represents an individual's belief related to their competency about successfully fulfilling career development tasks (Brown & Lent, 2019). Outcome expectations refer to beliefs about outcomes expected (positive or negative) after displaying the adaptive career behavior (Lent et al., 2017). According to a meta-analysis study, individuals with positive beliefs related to career development and positive expectations about the outcomes of their efforts have increased active participation in decisions related to career development and experience reduced career indecisiveness (Choi et al., 2012). Similarly, research showed that self-efficacy and outcome expectations positively related to decisional outcomes (e.g., decisional stress, choice certainty) (Lent et al., 2022), exploratory goals and career decidedness (Lent et al., 2016, 2019).

According to the CSM model, goals refer to people's intention to use adaptive career behaviors (Lent et al., 2017). Goal determination and efforts made in line with goals increase the probability of individuals achieving their desired outcomes (Lent & Brown, 2013). Additionally, feedback received by individuals from those around them about their career goals may be effective on career behavior. If the feedback is positive, it may have the role of encouraging career exploratory (Lent et al., 2017). However, if there is negative career feedback, individuals may reduce career expectations by drawing attention to inconsistency between their present situation and career goals, and may give up on their career goals (Hu et al., 2017b). Moving from here, it is thought that career goal feedback would positively influence career decidedness.

Contextual and personality factors

Contextual effects have an important place between career barriers and supports. In this research, the focus is on career barriers in terms of contextual effects. Career barriers occur due to personal or contextual-sourced reasons and are factors which make it more difficult to achieve career goals (Lent et al., 2000). The form of perception of these barriers may have a role in preventing confidence and positive attitudes about career development (Lent & Brown, 2013). The inverse of this situation may also be valid. Stated differently, expectations related to contextual barriers are based on self-efficacy beliefs about coping with these barriers. For this reason, individuals with high self-efficacy beliefs are more likely to perceive themselves as being able to cope with barriers encountered during career development (Lent et al., 2000). Contrary to this, individuals with low self-efficacy beliefs may have lower self-confidence about overcoming career barriers and may perceive career barriers as being more hazardous. In fact, research found a negative correlation between self-efficacy and perceived career barriers (Lent et al., 2008; Mejia-Smith & Gushue, 2017). On the other hand, career barriers carry a risk that leads individuals to eliminate career goals without turning them into action (Lent & Brown, 2020). Therefore, when individuals are freed of barriers that may limit their activities, the probability of adaptive career behaviors increases, including goal determination and implementing these goals (Lent & Brown, 2013). Similarly, research showed that career barrier perception negatively related to career decidedness. According to the research, individuals with low perception of deficient skills and time/financial resources had higher career decidedness (Toyokawa & DeWald, 2020).

Additionally, personality traits in the CSM model are assumed to affect career adjustment by activating the individual's affective coping tendencies and making behavioral performance easier (Lent & Brown, 2013). Studies found positive correlations between a variety of personality traits with CDMSE (Ojeda et al., 2012), career goals (Hirschi, 2010), choice certainty (Lent et al., 2022), and career decidedness (Lounsbury et al., 2005; Penn & Lent, 2019). Among these five-factor personality traits, individuals with high conscientiousness levels may approach the career research process with more self-confidence and optimism due to higher probability of determining career goals, and being organized and persistent (Lent et al., 2016).

The current study

The majority of research based on the CSM relating to career decisions of students in the university period was performed in Western cultures (Ireland & Lent, 2018; Lent et al., 2016, 2019). Additionally, there are limited numbers of studies in the literature relating to factors affecting adaptive career behaviors used with the aim of achieving career goals by individuals in non-Western cultures. Examples may be given as investigation of socio-cognitive factors positively related to job satisfaction of teachers in the United Arab Emirates (Badri et al., 2013) and academic satisfaction of Turkish university students (Işık et al., 2018). It appears that this model has not been tested in terms of career development in cultures with more collectivist tendencies (Cukur et al., 2004) like Turkey and it is considered that intercultural testing will significantly contribute to the field of psychology. In addition to the cultural structure of Turkey, it will be significant to know which variables are important for career decidedness of university students for services offered within the scope of career psychological counseling in terms of economic conditions. In developing countries like Turkey, the focus has been on long-term unemployment and work losses (unemployment rate for the young population including 15–24-year age group 18.09%, employment rate 48.5%; Turkish Statistical Institute, 2022), instead of the 21st century and adjustment to this period. From this perspective, this research aimed to test a model related to socio-cognitive variables affecting career decidedness of Turkish university students.

In testing the CSM, we first hypothesized that CDMSE, vocational outcome expectations, and career goal feedback (cognitive-person factors) would positively influence career decidedness (Adaptive career behaviors; Hypothesis 1, 2, and 3); and among the contextual and personality factors, career barriers would negatively influence career decidedness and conscientiousness would positively influence career decidedness (Hypothesis 4 and 5). Second, we hypothesized that conscientiousness would positively and indirectly influence (via CDMSE and career goal feedback, respectively) career decidedness (Hypothesis 6 and 7); CDMSE would positively and indirectly influence (via vocational outcome expectations and career barriers, respectively) career decidedness (Hypothesis 8 and 9); and conscientiousness would positively and indirectly influence (via the combination of CDMSE and vocational outcome expectations, the combination of vocational outcome expectations and career goal feedback, respectively) career decidedness (Hypothesis 10 and 11). The relevant hypothesis model is presented in Figure 1.

Figure 1.

Hypothesized test model: CSM model.

Method

This study had a cross-sectional design.

Participants

The study group for the research comprised a total of 445 university students attending a variety of departments in different faculties of a state university located in İstanbul province in the 2020–2021 educational year. Firstly, the Mahalanobis distance was calculated using the AMOS software to determine extreme values. Mahalanobis distance values were found to be between 0.13 (p > 0.05) and 41 (p < 0.01). According to the results, 17 extreme values were removed from the data set.

After removing outlier values, analyses were completed for the remaining 428 university students. The proportion of female students included in the scope of the research was 78%, with male student proportion of 22%. For students participating in the research, 32.5% were attending the preparation year or first year, 17.5% were in second year, 29.4% were in third year, and 20.6% were in fourth year or higher levels. The ages of students varied from 18 to 54 years, with mean age of 21.33 (sd = 3.96).

Measures

Career decision-making self-efficacy scale-short form (CDMSE-SF)

The CDMSE-SF was developed by Betz et al. (1996) with the aim of measuring the beliefs of individuals about their ability to successfully complete tasks required to make career decisions. It has a 5-point Likert type rating (1—no confidence at all and 5—complete confidence) and comprises 25 items (e.g., accurate assessment of talents, prepare a good resume). Turkish adaptation of the scale was developed by Büyükgöze-Kavas (2011) and internal consistency coefficient for the scale was 0.92, with test-retest reliability of 0.91. Confirmatory factor analysis was performed with the aim of testing the validity of the scale in the adaptation study and the four-factor model had good model fit for Turkish data. Validity was tested with similar scales and significant correlations were found for the general self-efficacy scale at 0.65 level, and the career decision scale indecision subdimension at −0.50 level (Büyükgöze-Kavas, 2011). The Cronbach alpha coefficient for the CDMSE-SF was calculated with data obtained in this study and found to be 0.94.

Vocational outcome expectation scale (VOES)

The VOES was developed by McWhirter et al. (2000) with the aim of measuring expectations about long-term outcomes of decisions made by individuals related to career development. It has 4-point Likert rating (1—completely disagree to 4—completely agree) and comprises six items (e.g., “My career planning will lead to a satisfying career for me” and “I will be successful in my chosen career/occupation”). Increased points obtained from the scale show high vocational outcome expectations. Turkish adaptation was performed by Işık (2010) and test-retest reliability coefficient was 0.79 and Cronbach alpha internal consistency coefficient was 0.87 for the scale. Confirmatory factor analysis was performed within the scope of validity studies and the single-factor structure of the scale was confirmed. Additionally, the scale validity was tested and the scale had significant correlation at 0.59 level with the Career Decision-Making Self-Efficacy Expectation Scale-Short Form (Işık, 2010). The Cronbach alpha coefficient for the VOES was calculated with data obtained in this study and found to be 0.91.

The big five inventory (BFI)

The BFI conscientiousness subdimension was used to assess the conscientiousness levels of university students in Turkey. The inventory developed by Benet-Martinez and John (1998) was adapted to Turkish by Sümer et al. (2005). This inventory comprises five subdimensions with 44 statements (16 inverse) defining personality. The subdimensions are neuroticism, extraversion, conscientiousness, agreeableness, and openness to experience. The conscientiousness subdimension includes nine statements (four inverse) (e.g., reliable worker, persisting until task is completed). The inventory has 5-point Likert rating and is rated from 1—definitely disagree to 5—completely agree. The reliability for the subdimensions varied from 0.64 to 0.77. Confirmatory factor analysis applied for validity studies confirmed the five-factor structure of the scale (Sümer et al., 2005). The Cronbach alpha coefficient was calculated with data obtained in this study for the BFI conscientiousness subdimension and found to be 0.77.

Career decidedness scale (CDS)

The CDS was developed by Lounsbury et al. (1999). It is a 5-point Likert scale (1—strongly disagree to 5—strongly agree) with six items (three inverse) and a single dimension (e.g., I made a definite decision about my career, I experience fluctuations about which career I will choose). Turkish adaptation of the scale was performed by Akçakanat and Uzunbacak (2019) and internal consistency coefficient was 0.80 and test-retest reliability was calculated as 0.73. Confirmatory factor analysis within the scope of validity studies confirmed the single-factor structure of the scale. Additionally, the Lawshe formula was applied for scope validity and the scale was concluded to have adequate validity levels (Akçakanat and Uzunbacak, 2019). The Cronbach alpha coefficient was calculated for the CDS with data obtained in this scale and found to be 0.78.

Career goal feedback scale (CGFS)

The CGFS was developed by Hu et al. (2017a) with the aim of measuring feedback received by university students from external and internal sources about career goals. The scale has 5-point Likert rating (1—definitely disagree to 5—definitely agree) and comprises 24 items (two inverse) (e.g., receiving constructive career advice from others, creating my own strategies to reach my desired career). The increase in points obtained from the scale shows the individual does not have behavior consistent with career goals. The scale has six-factor structure with factors of external progress, external goal suitability, external how to improve, internal progress, internal goal suitability, and internal how to improve. The adaptation of the scale to Turkish was performed by Korkmaz and Kırdök (2019). In the adaptation study, the reliability of the scale sub-dimension varied from 0.89 to 0.94, with internal consistency coefficient of 0.94 calculated for the whole scale. Confirmatory factor analysis was applied within the scope of the validity study and the six-factor structure of the scale was confirmed. Additionally, validity was investigated with other similar scales and correlations were at 0.61 level for the career stress scale and 0.44 for the general self-efficacy scale (Korkmaz and Kırdök, 2019). The Cronbach alpha coefficient was calculated for the CGFS with data obtained in this study and found to be 0.94.

Career barriers inventory (CBI)

The CBI was developed by Ulaş and Kızıldağ (2019) with the aim of measuring career barriers perceived by university students. This scale comprises 18 items (e.g., I have to plan my career according to my family's choice). It has 5-point Likert type (1—definitely disagree to 5—definitely agree) and contains four sub-dimensions. The sub-dimensions are attitudinal barriers, interaction barriers, social barriers, and educational barriers. Increases in points show the perceptions of the career barrier are high. The internal consistency coefficient for the sub-dimensions varied from 0.79 to 0.96, with test-repeat test reliability varying from 0.65 to 0.93. Exploratory and confirmatory factor analysis was performed within the scope of validity studies. Exploratory factor analysis showed the scale had a structure of four factors. Confirmatory factor analysis results showed the scale had acceptable fit indexes; χ2 (113) = 387.09, p < 0.001, RMSEA = 0.07, CFI = 0.92 (Ulaş & Kızıldağ, 2019). The Cronbach alpha coefficient was calculated for the CBI with data obtained in this study and found to be 0.86.

Demographic information form (DIF)

The DIF was prepared by the researchers with the aim of collecting information about the demographic characteristics of students. The DIF included questions about the age, sex, department attended, and class level of university students.

Procedure

Before beginning the data collection process in this study, ethics committee permission was obtained from İstanbul University-Cerrahpaşa Rectorate Social and Humanities Science Research Ethics Committee Chair dated 10.11.2020 and numbered 74555795-050.01.04. The data collection process was completed from November 2020 to May 2021 online. Within this scope, firstly a Google form was prepared and announcements were made to students attending a variety of faculties in a state university in İstanbul through social networks like Facebook and through lecturers providing lessons. Participants were first asked whether they accepted participation in the research and if they agreed they could continue; this choice was the first at the start of the form. Participants volunteering to participate in the research were informed about their rights related to confidentiality and volunteering, that they could end the research at any time, that responses would be evaluated in total, and that no identifying information was required.

Analysis

Data were analyzed with AMOS and SPSS. During administration of the scale the Google form had all questions marked as mandatory, so there were no missing values in the dataset and missing values analysis was not performed. For analysis of data, a structural equation model (SEM) was used. SEM is used in order to reveal whether relationship patterns and variables are verified or not by the data (Hayes, 2013). Maximum likelihood was used as the estimation method (Olsson et al., 2000). When the fit values related to the SEM are investigated, acceptable fit is shown by χ²/sd value being close to 3, CFI value between 0.90 and 95, GFI value between 0.85 and 0.90, AGFI and TLI values larger than 0.90, and RMSEA and SRMR values between 0.05 and 0.08 (Hu & Bentler, 1999; Kline, 2011; Schumacher & Lomax, 2004). Additionally, Hair et al. (2010) stated that there may be an increase in these values when the number of participants exceeds 250. Based on this, these criteria were taken as basis.

Results

Correlation coefficients between variables are given in Table 1. When Table 1 is investigated, correlation coefficients between variables appeared to vary from −0.60 to 0.76. Additionally, variables did not have correlation coefficient above 0.80 and for this reason, it appears the dataset abided by the multicollinearity assumption (Tabachnick & Fidell, 2013). As stated in Table 1, the correlations of the variables with each other were found to be significant. Before test analysis of the model, the correlational coefficients related to the variables are given in Table 1.

Table 1.

Correlation coefficients related to indicator variables.

Variables	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	18	19
CDMSE- A	1
CDMSE-B	0.64**	1
CDMSE-C	0.74**	0.64**	1
CDMSE-D	0.76**	0.70**	0.76**	1
CDMSE-E	0.69**	0.53**	0.67**	0.70**	1
CGF-A	−0.33*	−0.31**	−0.35**	−0.37**	−0.35**	1
CGF-B	−0.29**	−0.27**	−0.40**	−0.34**	−0.30**	0.38**	1
CGF-C	−0.40**	−0.34**	−0.41**	−0.41**	−0.34**	0.34**	0.62**	1
CGF-D	−0.55**	−0.45**	−0.58**	−0.60**	−0.49**	0.53**	0.46**	0.54**	1
CGF-E	−0.48**	−0.43**	−0.49**	−0.57**	−0.38**	0.47**	0.27**	0.60**	0.64**	1
CGF-F	−0.47**	−0.40**	−0.54**	−0.49**	−0.41**	0.28**	0.50**	0.45**	0.46**	0.41**	1
CB-A	−0.46**	−0.37**	−0.53**	−0.53**	−0.43**	0.39**	0.42**	0.43**	0.61**	0.57**	0.42**	1
CB-B	−0.13*	−0.13**	−0.16**	−0.15**	−0.12*	0.11**	0.32**	0.30**	0.24**	0.27**	0.20**	0.41**	1
CB-C	−0.24**	−0.21**	−0.31**	−0.30**	−0.27**	0.38**	0.45**	0.33**	0.34**	0.44**	0.30**	0.58**	0.43**	1
CB-D	−0.25**	−0.26**	−0.32**	−0.36**	−0.31**	0.37**	0.28**	0.27**	0.47**	0.49**	0.19**	0.57**	0.31**	0.47**	1
CO	0.46**	0.39**	0.50**	0.51**	0.39**	−0.32**	−0.34**	−0.50**	−0.50**	−0.53**	−0.43**	−0.46**	−0.18**	−0.30**	−0.42**	1
VOE	0.60**	0.48**	0.64**	0.56**	0.54**	−0.30**	−0.42**	−0.34**	−0.49**	−0.43**	−0.57**	−0.45**	−0.17**	−0.35**	−0.26**	0.39**	1
CD	0.58**	0.39**	0.64**	0.51**	0.47**	−0.36**	−0.40**	−0.39**	−0.61**	−0.48**	−0.53**	−0.53**	−0.21**	−0.37**	−0.34**	0.46**	0.43**	1

CDMSE-A: career decision-making self-efficacy-self-appraisal; CSMDE-B: career decision-making self-efficacy-occupational information; CDMSE-C: career decision-making self-efficacy-goal selection; CDMSE-D: career decision-making self-efficacy-planning; CS-F: career decision-making self-efficacy-problem-solving; CGF-A: career goal feedback-external improvements; CGF-B: career goal feedback-external goal suitability; CGF-C: career goal feedback-external goal progress; CGF-D: career goal feedback-internal improvements; CGF-E: career goal feedback-internal goal suitability; CGF-F: career goal feedback-internal goal progress; CB-A: career barriers-attitudinal; CB-B: career barriers-interactive; CB-C: career barriers-social; CB-D: career barriers-educational; CO: total points of conscientiousness; VOE: total points of vocational outcome expectations; CD: total points of career decidedness.

*p < 0.05, **p < 0.01, ***p < 0.001.

Before completing analysis of the model, the normality levels, means, standard deviations, and Cronbach alpha values for the variables were calculated and are shown in Table 2. When Table 2 is investigated, the mean values for indicator variables for the CDMSE variable were between 16.85 (sd = 3.92) and 18.84 (sd = 3.53), mean values for indicator variables for the career goal feedback variable were between 7.11 (sd = 3.62) and 10.05 (sd = 4.35) and mean values for the indicator variables of the career barriers variable varied from 4.57 (sd = 2.15) to 11.26 (sd = 4.46). Additionally, the mean value for vocational outcome expectation was 38.04 (sd = 5.80), the mean value for conscientiousness was 34.14 (sd = 5.51), and the mean value for career decidedness was 21.39 (sd = 4.85). When the skewness and kurtosis coefficients are investigated, they varied from −.02 to 1.48. The skewness and kurtosis values being between −3 and +3 are accepted as showing the distribution is normal (Tabachnick & Fidell, 2013). The Cronbach alpha reliability coefficients related to the variables were above 0.70 (Nunnally, 1978). Based on the fact that all values met the assumptions for testing the model (Hayes, 2013; Tabachnick & Fidell, 2013), the test stage for the measurement model began.

Table 2.

Descriptive statistics for variables.

Variables	$\bar{x}$	Std. deviation	Skewness	Kurtosis	Cronbach alpha
Self-appraisal^a	18.84	3.53	−0.49	0.49	0.81
Occupational information^a	18.39	2.95	−0.18	0.18	0.63
Goal selection^a	18.14	3.58	−0.42	0.20	0.81
Planning^a	17.93	3.66	−0.46	0.15	0.81
Problem-solving^a	16.85	3.92	−0.20	−0.13	0.80
External improvements^b	9.27	3.94	0.48	−0.41	0.92
External goal suitability^b	10.05	4.35	0.44	−0.55	0.85
External goal progress^b	7.15	3.58	1.26	1.35	0.89
Internal improvements^b	7.11	3.62	1.32	1.48	0.91
Internal goal suitability^b	9.02	4.13	0.51	−0.52	0.72
Internal goal progress^b	8.17	3.58	0.68	−0.04	0.88
Attitudinal^c	11.26	4.46	1.09	0.53	0.79
Interactive^c	7.90	3.50	1.22	0.76	0.70
Social^c	6.88	3.08	1.15	0.71	0.64
Educational^c	4.57	2.15	0.58	−0.50	0.63
Vocational outcome expectations	38.04	5.80	−0.15	−0.02	0.91
Conscientiousness	34.14	5.51	−0.38	−0.03	0.76
Career decidedness	21.39	4.85	−0.28	−0.50	0.78

Career decision-making self-efficacy scale indicator variables.

Career goal feedback scale indicator variables.

Career barriers scale indicator variables.

Measurement model

Before testing the SEM, analysis results for testing the measurement model showed the findings related to the measurement model and fit coefficients for the model to the data were at acceptable levels (χ²/sd = 2.39, NNFI = 94, CFI = 0.92, GFI = 0.87, AGFI = 0.86, RMSEA = 0.071). Based on this, the measurement model has acceptable level of fit and this allows these variables to be tested together with SEM.

Structural model

In the second stage of SEM, the structural model was investigated after the measurement model. The model tested first in the research comprises the main hypothesis in the research and tested the hypothesis that “conscientiousness, career decision-making self-efficacy, vocational outcome expectations and career goal feedback would positively influence career decidedness and career barriers would negatively influence career decidedness.” The SEM results for the tested model are presented in Figure 2.

Figure 2.

Model A: standardized regression weights.

When the standardized path coefficients for the model given in Figure 2 are examined (χ²/sd = 6.041, CFI = 0.86, GFI = 0.84, AGFI = 0.79, TLI = 0.83, RMSEA = 0.11, SRMR = 0.11), the values and the RMSEA values do not appear to be within the desired intervals (Hair et al., 2010; Kline, 2011; Schumacher & Lomax, 2004). Based on this, modifications recommended by AMOS were investigated and it appeared modification was recommended for the conscientiousness dimension for many variables. For this reason, the conscientiousness indicator variable was removed, and the possible coefficients related to the hypothesis “career decision-making self-efficacy, vocational outcome expectations, career barriers and career goal feedback variables together significantly related to career decidedness” were investigated. The findings related to this model are given in Figure 3.

Figure 3.

Alternative model B: standardized regression weights.

When the fit coefficients and t values for the model given in Figure 3 are investigated, it appears that all fit coefficients for the model have good levels. Accordingly, χ²/sd = 3.75, CFI = 0.91, GFI = 0.88, AGFI = 0.82, TLI = 0.89, RMSEA = 0.08, and SRMR = 0.08. The general fit index values for the structural and alternative models are shown in Table 3.

Table 3.

General fit coefficients for alternative models.

Model	Chi-square/sd	GFI	AGFI	CFI	TLI	RMSEA	SRMR	$Δ χ^{2}$
Model A	6.04	0.84	0.79	0.86	0.83	0.11	0.11
Model B	3.75	0.88	0.84	0.91	0.89	0.08	0.08	244.09

When Table 3 is investigated, removing the conscientiousness personality trait from the model caused no significant deterioration in the general fit of the model for the model fit coefficients ( $Δ χ^{2} = 244.09, p > 0.05$ ). For this reason, Model B was determined to be the final model. When the direct effects in Model B are investigated, one unit increase in the CDMSE variable caused a 0.27-point increase in career decidedness (β = 0.17; z = 2.60; p < 0.01) and a one unit increase in vocational outcome expectations caused a 0.21-point increase in career decidedness (β = 0.26; z = 5.28; p < 0.001). However, a one unit increase in the career goal feedback variable caused a 0.50-point reduction in career decidedness (β = −0.33; z = −3.63, p < 0.001). Additionally, the career barrier variable did not appear to have direct relationship with career decidedness (β = −0.10; z = −1.11; p = 0.268).

In addition to the direct effects of the independent variables on the career decidedness variable, the indirect effects were investigated in the study. Within this scope, the “vocational outcome expectation,” “career barrier,” and “career goal feedback” variables were considered as mediating variables. With the aim of statistically testing the significance of indirect effects, the bootstrap (BC, 95%) method (1000) was applied with AMOS. The findings obtained as a result of this analysis are presented in Table 4.

Table 4.

Indirect effects.

Hypothesis	Mean direct effect (x→y)	Mean indirect effect	Result
CDMSE→career barriers→career decidedness	0.17*	0.74*	Partial mediation
CDMSE→vocational outcomes→career decidedness	−0.17**	0.056*	Partial mediation
CDMSE→vocational outcomes→goal feedback	−0.75*	0.36 (ns)	Full mediation
CDMSE→vocational outcomes→goal feedback→career decidedness	−0.65*	0.33 (ns)	Full mediation

*p < 0.05; **p < 0.01; ***p < 0.001; ns = not significant.

As a consequence, the effect of CDMSE on career decidedness via the career barriers variable was significant and it was partially mediated by career barriers. The effect of CDMSE on career decidedness via the vocational outcome variable was significant and it was partially mediated by vocational outcomes. The effect of CDMSE on goal feedback via the vocational outcome variable was significant and it was fully mediated by vocational outcomes. Lastly, the effect of CDMSE on career decidedness via vocational outcome and goal feedback variables was significant and fully mediated by vocational outcome and goal feedback variables.

Discussion and conclusion

The results of the research show that CDMSE, vocational outcome expectations, career barriers, and career goal feedback variables together positively related to career decidedness. Additionally, CDMSE positively related to career decidedness mediated by career barriers, vocational outcome expectations, and career goal feedback. Though these findings are generally compatible with theoretical expectations, there are some situations which don’t abide by certain theoretical predictions (see Lent & Brown, 2013; Lent et al., 2017). For example, it was determined that the conscientiousness personality trait did not significantly contribute to the model. This situation may be affected by students having increasing concerns about employment when faced with the increasing unemployment rates in Turkey. Conscientious individuals participate in career research activities and have a tendency to spend more time thinking about information obtained and planning (Lent et al., 2016). However, in situations with increased employment concerns, the beliefs and self-confidence of university students that planning behavior will overcome this situation reduce (Yaşar & Turgut, 2020), and increases occur in depression and hopelessness levels (Karakus, 2018). For this reason, university students in Turkey may deal with these affective problems more and may not be able to display conscientious behavior. Another reason may be explained by research findings related to the view that conscientiousness is more important for less complex jobs in the literature (Chung-Yan, 2010; Shaffer & Postlethwaite, 2013). Accordingly, conscientiousness was found to be a stronger predictor for routine work compared to mentally difficult work. For example, Shaffer and Postlethwaite (2013) explained their results with the theory of cognitive buffering, which suggests that the predictive validity of conscientiousness is moderated by the complexity of jobs so that conscientiousness has more influence on performance in low-complexity jobs. When the participants in the study are investigated, generally they were attending the education faculty, law faculty, and theology faculty, and conscientiousness may not be significant in this model as they may be employed in more cognitively difficult jobs after graduation.

Another unexpected result is the lack of significant correlation between career barriers and career decidedness and this result conflicts with the literature. Career barriers are defined as events or situations making it more difficult for individuals to progress in their careers (Swanson & Woitke, 1997, p. 446). Research shows that students perceiving higher career barriers have significantly low career decidedness (Toyokawa & DeWald, 2020) and a tendency to experience more career undecidedness (Constantine et al., 2005; Jaensch et al., 2015). However, as university students have not yet transitioned to the working world, they may not have adequate experience about career barriers and may not sufficiently perceive the effects of career barriers. Hence, it is an understandable finding that career barrier perceptions do not have a significant effect on career decidedness. For this reason, university students may develop perspectives about career barriers with the experience they obtain in the working world. Additionally, currently, career psychological counseling services in Turkey are focused on the transition from high school to university and in the form of preference counseling, in other words, with matching logic (Yesilyaprak, 2019), and are founded on Parson's approach. This situation may cause the direction of the career development process and the effects of career barriers on the process to be ignored.

The other correlations tested within the scope of the research were significant, as expected. Among these, the positive correlation between CDMSE and career decidedness is supported by study results completed with university students in the literature and showing CDMSE positively related to career decidedness (Li et al., 2019; Penn & Lent, 2019; Restubog et al., 2010). CDMSE represents the individual's belief about their ability to complete career development tasks and to make decisions related to their career (Betz et al., 1996). Individuals with high CDMSE have a tendency to report less indecision about career paths (Choi et al., 2012). For this reason, young people having high CDMSE is important in terms of being able to progress with sure steps in their future career plans especially in the transition to working life after graduation from university.

This study also determined a positive correlation between vocational outcome expectations and career decidedness. This finding supports the results of research by Betz and Voyten (1997) concluding that strong outcome expectations were associated with clearer career goals and goal-oriented behavior. Vocational outcome expectations affect the development of career interests and goals of individuals as they include expectations about the outcomes of career behavior. In this sense, individuals expecting to reach their desired outcomes as a result of career choices have increased confidence about career decisions and may act more decisively in their choices. Similarly, research by Bargmann et al. (2021) showed students’ beliefs in their abilities increased after the first year of university education and they were more hopeful and decisive about their career future with reduced desire to drop out in this way, which is compatible with these results.

Finally, in this study, CDMSE was discovered to be positively related to career decidedness mediated by vocational outcome expectations, career barriers, and career goal feedback. According to the SCCT, self-efficacy expectations are effective on the career goals and career behavior of individuals mediated by outcome expectations (Lent et al., 1994). In addition to this, the self-management model mentions that career behavior positively related to outcome variables (Lent & Brown, 2013). In this research, career decidedness was considered the outcome variable. In light of the theoretical relationships, university students with high career decision self-efficacy have more positive expectations about the outcomes of decisions they make related to careers. In this situation they ensure they receive more positive feedback about career goals and thus, have increased commitment to career decisions. However, the self-efficacy beliefs of individuals may play an effective role in career decidedness through forms of interpreting career barriers. According to the SCCT, career barriers carry relative meanings in terms of different individuals and individuals interpret career barriers affected by their own diverse traits. When the interpretation compensates for the negative effects of career barriers, this may positively affect the career behavior of the individual (Lent et al., 2000). Based on this, university students with high career decision self-efficacy have higher probability of perceiving themselves as being able to cope with career barriers they face and it is expected that this situation will be positively reflected in their career decidedness.

Despite the important contributions of the present study, there are some limitations. First, we tested the CSM in samples of participants who live in İstanbul. Thus, the results obtained from this study can only be generalized to groups with similar characteristics. Another limitation of the study is the different distributions among participants in terms of sex. Though different results are presented in terms of sex in the relevant literature, this may be considered a limitation of this study.

Our aim in testing the CSM was to test a model emphasizing the process aspect of career development based on the social cognitive approach in Turkey. In this sense, it is important to determine what the processes underlying adaptive career behavior, included among the most important skills for this century, are in developing countries like Turkey. In this sense, in countries like Turkey where approaches closer to Parson's perspective are applied, we determined how process models operate and we saw that career barriers and the conscientiousness personality trait were not significant in the Turkish sample. Future studies may re-test with target groups such as employees in the working world instead of university students, those with different cultural orientations or with experiences of some form of career transition.

Within the scope of the implementation dimension, developmental and preventive services may be offered in university Career Centers within the framework of this model. In career decision-making, programs and activities may be organized under the auspices of career psychological counseling based on the direct and indirect effects of CDMSE, career barriers, vocational outcome expectations, and goal feedback variables on career decidedness. Thus, instead of services provided in the decision stage based on Parson's ideas, in Turkey the opportunity may be provided for work in the process stage encouraging positive self-efficacy beliefs and outcome expectations, and focusing on determining goals and overcoming barriers and adaptable behavior related to these.

Footnotes

Declaration of conflicting interests

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

Funding

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

ORCID iD

Özlem Ulaş Kılıç

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