A Test of the Career Construction Theory Model of Adaptation with Turkish Final Year Undergraduate Students

Abstract

The primary purpose of this study was to test career construction theory’s model of adaptation among Turkish undergraduate students in their final year. Additionally, the Career Commitment Scale was adapted for Turkish university students to ensure its appropriateness as a measure in testing the model of adaptation. Data obtained from the first sample (172 undergraduate students) were used to adapt the Career Commitment Scale and data obtained from a second group (243 undergraduate students in their final year) was used to test the career construction theory model of adaptation. The results of the study support the career construction theory’s model of adaptation. Additionally, the eight-item model for the adapted Career Commitment Scale demonstrates good fit and acceptable internal consistency.

Keywords

career commitment career adaptability career construction theory non-Western society

Change in today’s world occurs at an unprecedented rate and impacts multiple aspects of living. Working in these uncertain times means that individuals must learn to adapt. Career adaptation, as developed in Western cultures, typically requires exploration and reflection regarding oneself and one’s work. However, the Turkish educational system tends to prioritize academic achievement as an entryway to professional choices. Indeed, career choices for individuals in Turkey can be enhanced or limited based on performance on entrance exams for high schools and universities (e.g., Sarıkaya & Khorshid, 2009). As such, high school students in Turkey focus on achieving high exam scores to enter good universities and less emphasis is placed on self-exploration (Vurucu, 2010). Also, because the unemployment rate for young adults (ages 15–25 years) in Turkey is high (27.4%; Turkish Statistical Institute, 2019), many assume that graduating from more prestigious universities will afford them more quality job opportunities (Ceylan et al., 2015). Furthermore, research studies in Turkey suggest sufficient direction is not given during career decision-making processes (Akoğlan et al., 2013) and many students do not have alternative career plans (Erus & Zeren, 2017).

Cultural context has far reaching implications for the career development field. For example, compared to the United States, Turkish culture has less individualistic values (Cukur et al., 2004) and different work values (e.g., affective autonomy, egalitarianism, harmony, and conservatism; Schwartz, 1999). Western models of career development give prominence to the centrality of work in one’s life, individualism versus collectivism, and personal versus contextual variables, and they lack integration of spirituality (Sultana, 2017).

Because career construction theory’s model of adaptation (Savickas, 2013; Savickas & Porfeli, 2012) was developed in the United States, the primary purpose of this study is to test the full sequence of the model within the context of Turkish culture. A limited number of studies have tested the full sequence of the model and have shown varying, but at least partial, support for it (Perera & McIlveen, 2017; Tokar et al., 2020; Zhuang et al., 2018). Specific to Turkey, a study by Buyukgoze-Kavas et al. (2015) conducted with Turkish university students tried to explain the model but did not test all four elements of it. Although Öztemel and Akyol (2021) found support for all four dimensions of the model in a sample of Turkish high school students, we chose to investigate the entire sequence of the career adaptation model with students in their final year of undergraduate study and therefore closer to embarking upon a career transition. Support for the model with Turkish students in their last year of university study could lead to better assessment of where and how to intervene to assist students in career adaptation. As part of the study, we adapted the Career Commitment Scale (CCS) to Turkish to serve as a measure of career adaptation.

Model of Career Adaptation

Career construction theory (CCT) views career development as an ongoing process of adaptation through which individuals subjectively construct their careers to express their self-concept and substantiate their goals (Savickas, 2013). Career adaptability, a central construct of CCT, represents the psychosocial resources one must draw upon to cope with and manage “current and anticipated tasks, transitions, [and/or] traumas in their occupational roles that to some degree large or small, alter their social integration” (Savickas & Porfeli, 2012, p. 662). The model of adaptation advanced in CCT posits that individuals’ career adaptivity influences their career adaptability; and in turn, career adaptability influences adapting responses and the adaptation results from those responses (Rudolph et al., 2017; Savickas, 2013; Savickas & Porfeli, 2012).

Career Adaptivity

Career adaptivity, or adaptive readiness, is one’s willingness to change or adapt when met with instability brought on by career developmental tasks and issues (Savickas & Porfeli, 2012). In this study, proactive personality served as a measure of career adaptivity. Proactive personality is a personal disposition not constrained by situational influences to take intentional action to produce change in one’s environment (Bateman & Crant, 1993) and has been used as a form of adaptivity in other studies (Hirschi et al., 2015; Rudolph et al., 2017). It positively relates to networking behaviors, career initiative, and other indicators of career success (Fuller & Marler, 2009; Tolentino et al., 2014) as well as career adaptability (Green et al., 2020).

Career Adaptability

The attitudes, beliefs, and competencies underlying career adaptability that form adapting strategies and behaviors are grouped into four dimensions of career adapt-abilities (concern, control, curiosity, and confidence) and relate strongly to culture and context (Savickas & Porfeli, 2012). Career concern involves having a future orientation to plan for one’s career; control includes the self-regulation to engage with career developmental tasks and transitions conscientiously and intentionally; curiosity requires one to be inquisitive about oneself, the world of work, and future possibilities; and confidence involves one’s belief in their ability to pursue their aspirations and cope with difficulties encountered (Savickas, 2013; Savickas & Porfeli, 2012). The variable of career adaptability has been operationalized with the Career Adapt-Abilities Scale (CAAS; Savickas & Porfeli, 2012), which has been used regularly in research and has been translated in over 20 countries (Tokar et al., 2020).

Adapting Responses

Utilization of career adapt-abilities shapes one’s adapting responses, which are the strategies and actions that one takes to adapt (Savickas & Porfeli, 2012). Adapting responses involved in constructing a career choice is measured by the Student Career Construction Inventory (SCCI; Savickas et al., 2018), which operationally defines adapting responses and was designed “to be a comprehensive and specific measure of student responses to the primary career construction tasks encountered by adolescents and emerging adults” (p. 139).

Career Adaptation

Adaptation results are the outcomes of adapting responses. Hirschi et al. (2015) suggested adaptation is “often measured in terms of career decidedness, career commitment, job satisfaction, and work success” (p. 3). Blau (1988) defined career commitment as “one’s attitude towards one’s vocation, including a profession” (p. 295) and it is conceptualized as the extent to which one identifies with and values their profession. Given this study’s target population of higher education students in Turkey in their senior year, we chose career commitment as an adaptation variable.

Life satisfaction was chosen as another variable to operationally define adaptation. Research findings have shown positive relationships between career adaptability and life satisfaction (Santilli et al., 2014) including amongst Turkish undergraduate students (Buyukgoze-Kavas et al., 2015). Also, in a metanalysis, Rudolph et al. (2017) supported the idea that effective career adaptation should positively influence not just career outcomes but also life outcomes due to spillover effects from work roles to other life roles.

Hypotheses

In testing the career construction model of adaptation, we first hypothesized that adaptivity (proactive personality) would positively influence career adaptability (career adaptabilities, Hypothesis 1); career adaptability would positively influence adapting responses (student career construction, Hypothesis 2); and career adapting responses would positively influence career adaptation (life satisfaction and career commitment; Hypothesis 3). Second, we hypothesized that adaptivity would positively and indirectly influence (via adaptability) adapting responses (Hypothesis 4); adaptability would positively and indirectly influence (via adapting responses) adaptation results (Hypothesis 5); and adaptivity would positively and indirectly influence (via the combination of adaptability and adapting responses) adaptation results (Hypothesis 6). Prior to testing this model, we translated and adapted the Career Commitment Scale (Blau, 1985) to Turkish and examined its content validity, construct validity and Cronbach Alpha reliability value to ensure its appropriateness as a measure for this study.

Method

Participants

Phase One

To adapt the CCS to Turkish for undergraduate students, data were collected from a separate group through non-probability convenience sampling. This group was comprised of 176 undergraduate students from various departments and different programs in two state universities in the north and southeast of Turkey during the fall semester of 2019. Data were discarded from four students who replied to less than half of the items. No multivariate outliers were found in the data set. Analyses were conducted on a final sample of 172 undergraduate students. This sample size was deemed sufficient based on Everitt’s (1975) suggestion that the ratio of the sample size to the number of items should be at least 10 (172 students/8 items = 21.5). Of the final sample, 49 (28.5%) were male and 123 (71.5%) were female; 132 were sophomores (8.6%), 35 were juniors (20.3%), and 105 were seniors (61%). The average participant age was 21.56 years (SD = 2.64) with a range of 18–42 years.

Phase Two

To test our hypothesized career adaptation model, another sample group was recruited through non-probability convenience sampling and consisted of 288 students in their senior year from various departments and different programs in one state university in central Turkey during the 2019–2020 academic year. We detected 45 multivariate outliers using the squared value of Mahalanobis Distance (D²) and removed these outliers from the data set, resulting in a final sample of 243 students. For multiple correlation tests, Tabachnick and Fidell (2013) recommended that N should be greater than 50 plus the number of independent variables multiplied by 8, which equaled 74 for this study. Thus, the sample size was deemed sufficient. Of the final sample, 74 (31%) were male and 169 (69%) were female. The distribution of seniors according to the department they attended was as follows: 84 in Education (35%), 72 in Science (30%), 32 in Engineering (13%), 28 in Law (12%), and 27 in Literature (11%). The average age was 22.02 years (SD = 1.24) with a range of 20–32 years.

Measures

All of the concordance coefficients for these studies were evaluated using the χ² goodness-of-fit test, comparative fit index (CFI), goodness-of-fit index (GFI), adjusted goodness-of-fit index (AGFI), Tucker-Lewis Index (TLI), standardized root mean square residual (SRMR), and root mean square error of approximation (RMSEA). Guidelines for acceptable fit included CFI, TLI, AGFI, and GFI values greater than .90; SRMR values less than .08; and RMSEA values less than .06 (Hu & Bentler, 1999). For sample sizes of less than 500, we utilized the following parameters to indicate adequate model fit: CFI >.90 and RMSEA and SRMR <.10 (Weston & Gore, 2006), and X²/df ≤5 (Wheaton et al., 1977).

Proactive Personality Scale

To measure proactive personality, we used the 10-item version (created by Seibert et al., 1999) of the proactive personality measure originally developed by Bateman and Crant (1993). The correlation between the 10-item and the full 17-item scales was .96, and Cronbach’s alpha was .86 for the shortened version (Seibert et al., 1999). The PPS is a single-factor scale consisting of 10 items using a 7-point Likert-type scale (1 = strongly disagree, 7 = strongly agree). Sample items include, “wherever I have been, I have been a powerful force for constructive change” and “I am constantly on the lookout for new ways to improve my life” (Seibert et al., 1999).

The PPS was adapted into Turkish context by Akın and Özcan (2015). Their confirmatory factor analysis (CFA) revealed the PPS Turkish version consisted of one factor with a total of 10 items and Cronbach’s alpha was .86 (Akın & Özcan, 2015). In the present study, CFA was run and the general concordance coefficients of the model were deemed adequate (χ² = 191.268; df = 65; χ²/df = 2.94; AGFI = .87; GFI = .89; CFI = .94; RMSEA = .09; SRMR = .07; TLI = .93).

Career Adapt-Abilities Scale

The CAAS (Savickas & Porfeli, 2012) was used to measure career adaptability. The CAAS consists of 24 items and four subscales that assess the psychosocial resources of career concern, career control, career curiosity, and career confidence. Respondents reply using a 5-point Likert-type scale (1 = not strong, 5 = strongest). Sample items include, “learning new skills” and “planning how to achieve my goals.” Utilizing exploratory factor analysis, Savickas and Porfeli (2012) determined the CAAS consisted of four factors with a total of 24 items. They also found the CAAS demonstrated metric invariance across 13 countries and scale items showed similar relations among the latent traits across countries. Cronbach’s alpha was .92 (total scale); subscale coefficients ranged from .74 to .85.

Kanten (2012) adapted the CAAS into Turkish using CFA to determine the Turkish version had four factors comprised of 19 items. Öncel (2014) found expected relationships between the Turkish version CAAS subscales and future time perspective, core self-evaluations, internal locus of control, neuroticism, proactive personality, and generalized self-efficacy. The CAAS Turkish version has internal consistency reliability ranging from .74 to .92 (Kanten, 2012; Öncel, 2014). In the present study, CFA was run and general concordance coefficients of the model were deemed adequate (χ² = 441.803; df = 146; χ²/df = 3.02; AGFI = .84; GFI = .87; CFI = .91; RMSEA = .08; SRMR = .06; TLI = .89).

Student Career Construction Inventory

The SCCI (Savickas et al., 2018) measures students' adapting responses to career construction tasks. It contains 18 items and four subscales that assess (1) crystallizing a vocational self-concept, (2) exploring occupations, (3) deciding on an occupational choice, and (4) preparing to implement that choice. Responses are on a 5-point Likert-type scale (1 = I have not yet thought much about it, 5 = I have already done this). Sample items include, “determining what values are important to me” and “setting goals for myself.” Exploratory factor analysis revealed four factors with a total of 18 items; CFA using a separate sample confirmed those results and multi-group CFA model results indicated the SCCI exhibited configural and measurement invariance (Savickas et al., 2018). The SCCI had expected correlations with the CAAS, the Career Maturity Inventory-Form C, and the Vocational Identity Scale among college students (Savickas et al., 2018). Cronbach's alpha coefficients were .93 for the total scale; subscales ranged from .84 to .94 (Savickas et al., 2018).

When adapted into Turkish culture, CFA indicated a three-factor structure was a better fit (Sevinç & Siyez, 2018). Hence, the Turkish form consists of three subscales that assess crystallizing a vocational self-concept, exploring occupations, and deciding on an occupational choice. Sevinç and Siyez (2018) reported significant relationships between the Turkish version of the SCCI and the CAAS. They also reported Cronbach’s alpha coefficients of .87 for the total scale, .65 for crystallizing, .72 for exploring, and .87 for deciding. Confirmatory factor analysis was run in the present study and general concordance coefficients of the model were deemed adequate (χ² = 395.294; df = 132; χ²/df = 2.98; AGFI = .89; GFI = .88; CFI = .92; RMSEA = .07; SRMR = .06; TLI = .90).

Career Commitment Scale

The CCS, developed by Blau (1985), measures career commitment, explained as one's attitude towards one's profession or vocation. The CCS is a single-factor scale consisting of 8 items and using a 5-point Likert-type scale (1 = strongly disagree, 5 = strongly agree) for responses. Sample items include, “I definitely want a career for myself in this profession” and “if I could get another profession different from this profession that paid the same amount, I would probably take it.” Exploratory factor analysis indicated the CCS had eight items and one factor (Blau, 1985). The CCS has internal consistency coefficients ranging from .83 to .87 on different occasions and with different samples (Blau, 1985; 1988).

Karavardar (2014) translated the CCS into Turkish and found a Cronbach’s alpha coefficient of .76. However, she did not run CFA and we were unsure that her version was appropriate for university students, thus we chose to adapt the CCS for Turkish university students. Further details about instrument adaptation and psychometric properties are reported in the Data Analysis and Results sections.

Satisfaction with Life Scale

To measure participants’ life satisfaction levels, we used the SWLS developed by Diener et al. (1985). This is a single-factor scale and consists of 5 items with responses provided on a 7-point Likert-type scale (1 = strongly disagree, 7 = strongly agree). Sample items include, “so far I have gotten the important things I want in life” and “the conditions of my life are excellent.” Diener et al. (1985) conducted exploratory factor analysis in which a single factor emerged accounting for 66% of the variance. They also found scores on the SWLS correlated .02 with the social desirability measure, suggestion it was not evoking a social desirability response set. Furthermore, Diener and colleagues found expected correlations of the SWLS with personality indicators of well-being and with the Life Satisfaction Index. Lastly, Cronbach’s alpha internal consistency and test re-test reliability coefficients were reported as .87 and .82, respectively.

Köker (1991) adapted the SWLS into Turkish context and provided evidence of test-retest reliability (.85). In addition, item-total correlations were calculated and ranged between .71 and .80. For additional evidence of the SWLS’s validity, Avşaroğlu and Koç (2019) performed CFA and found good fit indices. In the present study, CFA was run and the general concordance coefficients of the model were considered acceptable (χ² = 11.071; df = 5; χ²/df = 2.21; AGFI = .95; GFI = .98; CFI = .99; RMSEA = .06; SRMR = .05; TLI = .98).

Demographic Information Form

The DIF included questions about biological sex (as culturally accepted in Turkey), age, and major.

Procedures

Ethical committee permission was obtained for both phases of the study. We also received permission from Dr. Blau (developer of the CCS) to adapt the CCS.

In evaluating the adapted version of the CCS, we used data that were collected in an online format during class hours. Appropriate days and times for administration of the materials were determined via coordination with university instructors, and instructors administered the Google forms to students. During administration, appropriate explanations were provided and students were instructed on how to properly complete the assessments and forms. The study’s purpose, confidentiality, and the right to withdraw from the study at any time were clarified.

For the data collection process in testing the career construction model of adaptation, recruitment announcements were made through university websites and via faculty members to individuals who were in their senior year. Tools were administered online to volunteer participants via Google Forms in which the study’s purpose, confidentiality, and the right to withdraw from the study at any time were articulated.

Data Analysis

Data obtained from the two sample groups were analyzed via SPSS 18.0 and AMOS 22.0. For the study phase of adapting the CCS for Turkish university students, a missing data analysis found missing values comprised less than 5% of the values and were randomly distributed. We replaced the missing values with mean values using the expectation-maximization method. Little’s Test of Missing Completely at Random results (χ² = 20.21, df = 21, p = .510) showed that missing data in the data sets were randomly distributed (Rubin, 1987). In testing the career construction theory’s model of adaptation, we did not encounter any missing data from responses to mandatory questions.

For both phases of the study, maximum likelihood (ML) was used as an estimation method. Since the most common and recommended tests of mediation for various combinations of parameters is bootstrap (Hayes, 2013), we used a bootstrapping procedure to test the significance of the indirect effects.

Results

Adaptation of the CCS

Preliminary Analysis

We obtained the normality and linearity of the data for the first sample group used to adapt the CCS to Turkish. The univariate skewness of items ranged from −.046 to –1.435, and the univariate kurtosis ranged from .049 to 1.68. Per Tabachnick and Fidell (2013), the data were determined to be normally distributed.

Content Validity

To adapt the CCS, we followed defined steps throughout the translation process. First, we sent the original items to five experts with doctoral-level degrees in counseling and asked each of them to independently translate the inventory into Turkish. Second, we compared the five expert translations and chose the best translation for each item. Third, we asked three psychological counselors and one English expert to back-translate the Turkish items into English. Fourth, we worked with two assistant professors of psychological counseling and an associate professor of English to choose the translations that were closest to the original items. For items that were under debate, the group brainstormed ways to check understandability, clarity, and cultural appropriateness of the items and used those methods to reach a consensus. Based on their feedback, we then made minor changes to the second item of the scale (revised from “I definitely want a career for myself in this profession” to “I am definitely planning a career for myself in this profession”) and the sixth scale item (revised from “I am disappointed that I ever entered this profession” to “I am disappointed that I chose this profession”).

Next, we created an index to measure the validity of each item and of the total scale. In this process, we shared each original and translated item with nine reviewers (six with doctoral-level degrees in counseling and three experts in English). These reviewers rated each translation on a scale from 1 (not relevant) to 4 (very relevant) to assess how relevant the translated items were to the corresponding original items. We then entered these reviewer ratings into an Excel worksheet that we used to compute the item-level and scale-level content-validity index. We found no items with content-validity scores below the generally accepted cutoff value of .78 (Polit & Beck, 2006). The items’ content-validity scores ranged from .88 to 1.00 and the CVI of the total instrument was .98. We consequently accepted the item translations.

Construct Validity

Within the construct of CCS’s validity, we applied CFA with maximum likelihood estimation (ML). The overall concordance coefficients of the CCS model were χ² = 54.722; df = 20; χ²/df = 2.73; AGFI = .90; GFI = .93; CFI = .93; RMSEA = .10; TLI = .90. The concordance values established that the model resulted in adequate fit according to guidelines from Hu and Bentler (1999), Weston and Gore (2006), and Wheaton et al. (1977).

To improve the model, we examined the modification index for each parameter that AMOS recommended. Error variances of two items were related, “I am disappointed that I ever entered this profession” (e2) and “If I could do it all over again, I would not choose to work in this profession” (e6); these two items were more similar to each other in terms of emphasizing the nonideality of the choices. Consequently, covariance was added between the two items per AMOS modification suggestions. The general concordance coefficients of the modified model were χ² = 30.499; df = 19; χ²/df = 1.61; AGFI = .93; GFI = .96; CFI = .98; RMSEA = .06; TLI = .96, which significantly improved fit (Δχ² = 24.22, p < .001). Thus, the alternative model (see Figure 1) fit the data significantly better than did the hypothesized model. The concordance values of the alternative model confirmed that general compliance was high according to Hu and Bentler (1999), Weston and Gore (2006), and Wheaton et al. (1977).

Reliability

Cronbach’s alpha was .84.

Testing the Career Construction Theory Model of Adaptation

Preliminary Analysis

All data for these results were obtained from the second sample group. Two latent and 10 indicator variables existed in the tested career construction theory model of adaptation. To test the model of adaptation, multi-collinearity, normality, linearity, and Cronbach’s alpha values were analyzed and all assumptions were met (see Table 1).

Table 1.

Descriptive Statistics and Correlation Coefficients Related to Indicator Variables.

Indicator Variables	1	2	3	4	5	6	7	8	9	10
Proactive personality	1
Concern^a	.46*	1
Control^a	.43*	.69*	1
Curiosity^a	.50*	.68*	.66*	1
Confidence^a	.49*	.68*	.72*	.78*	1
Self-concept crystallization^b	.50*	.56*	.58*	.59*	.56*	1
Occupational exploration^b	.35*	.48*	.49*	.55*	.53*	.63*	1
Career Decision-making^b	.41*	.67*	.64*	.55*	.56*	.66*	.66*	1
Life satisfaction	.44*	.49*	.53*	.40*	.45*	.54*	.41*	.57*	1
Career commitment	.19*	.43*	.33*	.37*	.31*	.30*	.33*	.51*	.41*	1
M	46.95	22.12	23.26	22.18	23.44	22.34	11.67	34.60	23.46	27.94
SD	14.83	4.78	4.46	4.44	4.42	4.40	2.52	7.11	6.45	7.21
S	-.67	-.36	-.61	-.29	-.49	-.49	-.67	-.75	-.48	-.31
K	-.46	-.50	-.06	-.49	-.33	-.15	.15	.03	-.37	-.57
Α	.97	.90	.86	.88	.89	.83	.80	.93	.89	.89

*p < .001.

^aCareer Adaptabilities Scale indicator variables.

^bStudent Career Construction Inventory indicator variables.

Measurement Model

We tested the measurement model to determine to what extent the indicator variables were effective in accounting for the latent variables and to evaluate the relationship among the latent variables. Confirmatory factor analysis results revealed that the measurement model fit the data acceptably, χ2 (395, N = 243) = 1024.28, p < .001, CFI = .90, SRMR = .07, and RMSEA = .08. Additionally, all factor loadings were significant (p < .001). In examining criteria established by Hu and Bentler (1999), Weston and Gore (2006), and Wheaton et al. (1977) related to model coefficient of concordance obtained from testing the model, we concluded that the latent variables in the model were represented by the indicator variables.

Structural Model

The hypothesized structural model included direct paths from proactive personality to career adaptability, from career adaptability to student career construction, and from student career construction to life satisfaction and career commitment. The structural model coefficients were χ² (34, N = 243) = 124.12, p < .001, CFI = .93, SRMR = .05, RMSEA = .09, 90% CI [.08, .11]. The concordance values of the model showed that the general compliance was an adequate fit per guidelines from Hu and Bentler (1999), Weston and Gore (2006), and Wheaton et al. (1977).

As a result, when direct effects were investigated (see Figure 2), proactive personality had a significant and positive direct effect on career adaptability; career adaptability had a significant and positive direct effect on student career construction; and student career construction had significant and positive direct effects on life satisfaction and career commitment. The variables explained 33% of the variance in career adaptability, 70% of student career construction, 43% of life satisfaction, and 26% of career commitment see Figure 2

We also considered an alternative model that included additional direct paths from proactive personality to student career construction, and from proactive personality and career adaptability to life satisfaction and career commitment. Results indicated that χ² (29, N = 243) = 113.473, p < .001, CFI = .94, SRMR = .04, RMSEA = .11, 90% CI [.09, .13). The chi-square difference test showed that the alternative model did not fit the data significantly better than did the hypothesized model (Δχ² (5, N = 243) = 10.65, p > .05). Moreover, all direct relationships included in the hypothesized model were significant in the alternative model, while none of the additional direct effects were significant. Though the RMSEA value in the hypothesis model appeared to be close to the cut-off criteria, it is important to consider that the RMSEA value is very closely related to sample size and hence degrees of freedom (Weston & Gore, 2006). Consequently, the RMSEA value appeared to be a test of close fit and, overall, results supported the decision to retain the hypothesized model.

Indirect Effects

The significance of the indirect effects of mediator variables was analyzed with the assistance of the bootstrap test. Whereas the relationships between proactivity and student career construction and proactivity and career commitment were fully mediated by adaptability, the relationship between proactivity and life satisfaction was only partially mediated by adaptability. Also, the relationship between proactivity and career commitment was fully mediated by student career construction, but the relationship between proactivity and life satisfaction was only partially mediated by student career construction. Relationships between adaptability and career commitment and adaptability and life satisfaction were fully mediated by student career construction. The sequential pathway of “proactivity - career adaptability - student career construction - life satisfaction” was significant (indirect effect = 0.119, standard error of mean = 0.123, 95% CI = [0.067, 0.210]). The sequential pathway of “proactivity - career adaptability - student career construction - career commitment” was significant (indirect effect = 0.117, standard error of mean = 0.04, 95% CI = [0.054, 0.209]). Consequently, the full model of the career construction model of adaptation was tested and all significant indirect effects were consistent with said model see Table 2.

Table 2.

Indirect Effects.

Indirect Path	Mean Direct Effect	Mean Indirect Effect	Result
Proactivity-adaptability-student career construction	.08 (ns)	.44**	Full mediation
Proactivity-adaptability-career commitment	-.10 (ns)	.30**	Full mediation
Proactivity-adaptability-life satisfaction	.15*	.29**	Partial mediation
Proactivity-student career construction-career commitment	-.10 (ns)	.30**	Full mediation
Proactivity-student career construction-life satisfaction	.15*	.29**	Partial mediation
Adaptability-student career construction-career commitment	.03 (ns)	.42**	Full mediation
Adaptability-student career construction-life satisfaction	-.05 (ns)	.49**	Full mediation

*p < .05; **p < .01; ns = not significant.

Discussion

Adapted Version of CCS

The psychometric properties of the CCS translated to Turkish indicated that the 8-item and one-factor CCS demonstrated a good fit to the data, similar to factor analytic results of Blau’s (1985) original study. Also consistent with results of previous studies (Blau, 1985; 1988; Karavardar, 2014), findings indicated a high Cronbach’s alpha coefficient (George & Mallery, 2003). Thus, the CCS Turkish version had acceptable evidence for reliability and validity within the scope of this study.

Career Adaptation Model

Results support the career construction model of adaptation in that there are direct impacts between the sequential steps of the model. This is in line with findings of previous studies conducted with university students (e.g., Hirschi et al., 2015; Perera & McIlveen, 2017).

Implications

The model of adaptation from career construction theory can be useful in planning career interventions to support the career development of university students. Because the model is sequential, we suggest that each phase of the model be assessed and targeted as appropriate. For example, findings that proactive personality predicted career adaptability, career adapting responses, and adaptation outcomes suggest that practitioners help students develop proactive behaviors. This can involve career-related activities that encourage envisioning, planning, enacting, and reflecting (Bindl et al., 2012).

Development of career adapt-abilities is also of consequence given that career adaptability fully mediated the relationship between proactivity and students’ adapting responses as well as their levels of career commitment. Career counselors might enhance career adaptability by facilitating discovery and exploration of self and world while connecting those discoveries to future possibilities (career concern). Building one’s skills in self-regulation and sense of control over one’s intentions, actions, and decisions (which can include choosing to honor one’s cultural values) are potential ways to develop career control. Encouraging sensible risk-taking and inquiring behaviors may enhance career curiosity. Additionally, Hartung et al. (2008) advised role playing, social modeling, and cognitive-behavioral interventions aimed at increasing self-efficacy to stimulate career confidence.

Use of the CCS (Blau, 1985), and the adapted Turkish version used in this study, may lead to more targeted interventions based on one’s career commitment levels. Interventions can include exploring professions through volunteering or informational interviews and enhancing self-exploration with reflection on personal and cultural values.

Future research possibilities include testing the CCS with other sample groups across various cultures to further analyze intercultural differences as well as to examine the instrument’s discriminant and concurrent validity. Additionally, longitudinal analyses can examine whether university students participating in career development programs based on the model of adaptation make significant improvements in career adaptability. Also, longitudinal studies following students after graduation could provide meaningful information regarding their ongoing career adaptability.

Limitations

Despite the important contributions of the present study, there are some limitations. First, we tested the Turkish version of the CCS in samples of participants who live in the north and southeast of Turkey. Thus, the results obtained from this study can only be generalized to groups with similar characteristics. Another limitation of the study is the high percentage of female participants as compared to males. Ulaş (2016) discovered men tend to work in different jobs in their final year of university which may mean their attendance is lower (which would have affected their recruitment). Also, Tomul (2007) found the potential for an increase in income can positively affect female participation in education, especially in Central Anatolia (where this study was conducted). Another study limitation is that a single measure was used to measure each dimension of the model, apart from adaptation. Measurement of every dimension of the model with multiple measures may affect the model. Lastly, we used a cross-sectional design to test the research model. Therefore, causal inferences cannot be made about correlations among the variables.

Conclusion

It is critical to examine Western models of career development and counseling in other cultures. The results of this study indicated that our adaptation of the Career Commitment Scale to Turkish culture demonstrates good fit and has acceptable internal consistency. This study also provides evidence for career construction theory’s model of adaptation and we contend the model can be useful in the practice and research of career counseling in Turkey.

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

Author Biographies

Ozlem Ulas Kilic, PhD, is an Associate Professor in the Counseling and Guidance program at Giresun University in Giresun, Turkey. She completed her undergraduate education in Gazi University, Psychological Counseling and Guidance Department in 2009; her master's degree in 2011 and her doctorate in 2016 at Hacettepe University, Psychological Counseling and Guidance Department. In 2014, she was a visiting researcher in the Department of Counseling and Career Development at Colorado State University with a TUBITAK International Doctorate Research Scholarship. To complete her post-doctoral studies, she won the TUBITAK International Post-Doctoral Research Scholarship and worked as a Post-Doctoral Researcher at Colorado State University Counseling and Career Development Department in 2018–2020. She carries out her academic studies especially within the scope of career counseling and multiculturalism approach. Specifically, her areas of teaching and research interest include career decision-making self-efficacy, career transitions and career construction. In her spare time, she enjoys playing sports, hiking and reading biography books.

Jacqueline J. Peila-Shuster, PhD, LPC, NCC is an Associate Professor in the Counseling and Career Development graduate program at Colorado State University in Fort Collins, Colorado. She has a Ph.D. with an emphasis in Counselor Education, an M.Ed. specializing in Counseling and Career Development, and a B.S. in Occupational Therapy. Jackie teaches courses in career development, career counseling, and counseling skills; supervises counseling internship; and serves as the program chair as well as the career counseling specialization coordinator. Her areas of teaching and research interest include career counseling and development across the lifespan, counselor education, career construction, and life design. In her spare time, Jackie enjoys spending time with family and discovering new restaurants with her daughter.

References

Akın

A.,

Özcan

N. A.

(2015). Short Turkish version of proactive scale: A study of validity and reliability. Mevlana International Journal of Education, 5(1), 165–172. https://doi.org/10.13054/mije.14.12.5.1

Akoğlan Kozak

M.,

Dalkıranoğlu

(2013). Mezun öğrencilerin kariyer algılamaları: Anadolu Üniversitesi örneği [Career perceptions of new graduates: Anadolu University example]. Anadolu University Journal of Social Sciences, 13(1), 41–52.

Avşaroğlu

S.,

Koç

(2019). Yaşam doyumu ile sıkıntıyı tolere etme arasındaki ilişkide iyimserliğin aracı etkisi [The mediation effect of optimism on the relationship between life satisfaction and distress tolerance]. Türk Psikolojik Danışma Ve Rehberlik Dergisi, 9(53), 565–588.

Bateman

T. S.,

Crant

J. M.

(1993). The proactive component of organizational behavior: A measure and correlates. Journal of Organizational Behavior, 14(2), 103–118. https://doi.org/10.1002/job.4030140202

Bindl

U. K.

Parker

S. K.

Totterdell

Hagger-Johnson

(2012). Fuel of the self-starter: How mood relates to proactive goal regulation. Journal of Applied Psychology, 97(1), 134–150. https://doi.org/10.1037/a0024368

Blau

G. J.

(1985). The measurement and prediction of career commitment. Journal of Occupational Psychology, 58(4), 277–288. https://doi.org/10.1111/j.2044-8325.1985.tb00201.x

Blau

G. J.

(1988). Further exploring the meaning and measurement of career commitment. Journal of Vocational Behavior, 32(3), 284–297. https://doi.org/10.1016/0001-8791(88)90020-6

Buyukgoze-Kavas

Duffy

R. D.

Douglass

R. P.

(2015). Exploring links between career adaptability, work volition, and well-being among Turkish students. Journal of Vocational Behavior, 90(1), 122–131. https://doi.org/10.1016/j.jvb.2015.08.006

Ceylan

Coştur

Çiffiliz-Kınay

Demirhan

Demirtaş-Zorbaz

Dinçel

Doğan

Ersel

Kayhan

Kelecioğlu

Ulaş

Ö.

(2015). Üniversite ve meslek seçimini etkileyen etmenler. Hacettepe Üniversitesi Yayınları.

10.

Cukur

C. S.

de Guzman

M. R.

Carlo

(2004). Religiosity, values, and horizontal and vertical individualism-collectivism: A study of Turkey, the United States, and the Philippines. The Journal of Social Psychology, 144(6), 613–634. https://doi.org/10.3200/socp.144.6.613-634

11.

Diener

Emmons

R. A.

Larsen

R. J.

Griffin

(1985). The satisfaction with life scale. Journal of Personality Assessment, 49(1), 71–75. https://doi.org/10.1207/s15327752jpa4901_13

12.

Erus

S. M.,

Zeren

Ş. G.

(2017). Öğretmen adaylarının öğretmenlik mesleğine yönelik kariyer uyumları [Preservice teachers' career adaptability towards teaching profession]. Journal of Higher Education and Science, 7(3), 657–668. https://doi.org/10.5961/jhes.2017.242

13.

Everitt

B. S.

(1975). Multivariate analysis: The need for data, and other problems. British Journal of Psychiatry, 126, 237–240. https://doi.org/10.1192/bjp.126.3.237

14.

Fuller

Jr., Marler

L. E.

(2009). Change driven by nature: A meta-analytic review of the proactive personality literature. Journal of Vocational Behavior, 75(3), 329–345. https://doi.org/10.1016/j.jvb.2009.05.008

15.

George

D.,

Mallery

(2003). SPSS for windows step by step: A simple guide and reference. 11.0 update (4th ed.). Allyn & Bacon.

16.

Green

Z. A.

Noor

Hashemi

M. N.

(2020). Furthering proactivity and career adaptability among university students: Test of intervention. Journal of Career Assessment, 28(3), 402–424. https://doi.org/10.1177/1069072719870739

17.

Hartung

P. J.

Porfeli

E. J.

Vondracek

F. W.

(2008). Career adaptability in childhood. The Career Development Quarterly, 57(1), 63–74. https://doi.org/10.1002/j.2161-0045.2008.tb00166.x

18.

Hayes

A. F.

(2013). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach. Guilford Press.

19.

Hirschi

Herrmann

Keller

A. C.

(2015). Career adaptivity, adaptability, and adapting: A conceptual and empirical investigation. Journal of Vocational Behavior, 87(1), 1–10. https://doi.org/10.1016/j.jvb.2014.11.008

20.

L.,

Bentler

P. M.

(1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55. https://doi.org/10.1080/10705519909540118

21.

Kanten

(2012). Kariyer uyum yetenekleri ölçeği: Geçerlilik ve güvenilirlik çalışması [career adapt-abilities scale: A study of validity and reliability]. Süleyman Demirel Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 16, 191–205.

22.

Karavardar

(2014). Career commitment, subjective career success and career satisfaction in the context of hazelnut processing industry in Giresun/Turkey. International Journal of Business and Management, 9(6), 98–105. https://doi.org/10.5539/ijbm.v9n6p98

23.

Köker

(1991). Normal ve sorunlu ergenlerin yaşam doyumu düzeyinin karşılaştırılması [Comparison of life satisfaction levels of normal and problematic adolescents. Ankara University.

24.

Öncel

(2014). Career adapt-abilities scale: Convergent validity of subscale scores. Journal of Vocational Behavior, 85(1), 13–17. https://doi.org/10.1016/j.jvb.2014.03.006

25.

Öztemel

K.,

Akyol

E. Y.

(2021). From adaptive readiness to adaptation results: Implementation of Student Career Construction Inventory and testing the career construction model of adaptation. Journal of Career Assessment, 29(1), 54–75. https://doi.org/10.1177/1069072720930664

26.

Perera

H. N.,

McIlveen

(2017). Profiles of career adaptivity and their relations with adaptability, adapting, and adaptation. Journal of Vocational Behavior, 98(1), 70–84. https://doi.org/10.1016/j.jvb.2016.10.001

27.

Polit

D. F.,

Beck

C. T.

(2006). The content validity index: Are you sure you know what’s being reported? Critique and recommendations. Research in Nursing & Health, 29(5), 489–497. https://doi.org/10.1002/nur.20147

28.

Rubin

D. B.

(1987). Multiple imputation for nonresponse in surveys. John Wiley & Sons.

29.

Rudolph

C. W.

Lavigne

K. N.

Zacher

(2017). Career adaptability: A meta-analysis of relationships with measures of adaptivity, adapting responses, and adaptation results. Journal of Vocational Behavior, 98(1), 17–34. https://doi.org/10.1016/j.jvb.2016.09.002

30.

Santilli

Nota

Ginevra

M. C.

Soresi

(2014). Career adaptability, hope and life satisfaction in workers with intellectual disability. Journal of Vocational Behavior, 85(1), 67–74. https://doi.org/10.1016/j.jvb.2014.02.011

31.

Sarıkaya

T.,

Khorshid

(2009). Üniversite öğrencilerinin meslek seçimini etkileyen etmenlerin incelenmesi: Üniversite öğrencilerinin meslek seçimi [the investigation of the factors that affect university students’ profession choice: University students’ profession choice]. Turkish Educational Sciences Journal, 7(2), 393–423.

32.

Savickas

M. L.

(2013). Career construction theory and practice. In Brown

S. D.

Lent

R. W.

(Eds), Career development and counseling: Putting theory and research to work (2 nd ed., pp. 147–183). John Wiley & Sons.

33.

Savickas

M. L.,

Porfeli

E. J.

(2012). Career Adapt-Abilities Scale: Construction, reliability, and measurement equivalence across 13 countries. Journal of Vocational Behavior, 80(3), 661–673. https://doi.org/10.1016/j.jvb.2012.01.011

34.

Savickas

M. L.

Porfeli

E. J.

Hilton

T. L.

Savickas

(2018). The student career construction inventory. Journal of Vocational Behavior, 106(1), 138–152. https://doi.org/10.1016/j.jvb.2018.01.009

35.

Schwartz

S. H.

(1999). A theory of cultural values and some implications for work. Applied Psychology: An International Review, 48(1), 23–47. https://doi.org/10.1111/j.1464-0597.1999.tb00047.x

36.

Seibert

S. E.

Crant

J. M.

Kraimer

M. L.

(1999). Proactive personality and career success. Journal Of Applied Psychology, 84(3), 416–427. https://doi.org/10.1037/0021-9010.84.3.416

37.

Sevinç

A.,

Siyez

D. M.

(2018). Öğrenci kariyer yapılandırma envanteri’nin türkçe uyarlaması: Geçerlik ve güvenirlik çalışması [adaptation of the student career construction inventory into Turkish: A validity and reliability study]. İş Ve İnsan Dergisi, 5(2), 157–168.

38.

Sultana

R. G.

(2017). Anchoring career guidance in the mediterranean? In search of southern perspectives. In Sultana

R. G.

(Ed), Career guidance and livelihood planning across the Mediterranean (pp. 3–15). Sense.

39.

Tabachnick

B. G.,

Fidell

L. S.

(2013). Using multivariate statistics. Pearson Education.

40.

Tokar

D. M.

Savickas

M. L.

Kaut

K. P.

(2020). A test of the career construction theory model of adaptation in adult workers with Chiari Malformation. Journal of Career Assessment, 28(3), 381–401. https://doi.org/10.1177/1069072719867733

41.

Tolentino

L. R.

Garcia

P. R. J. M.

V. N.

Restubog

S. L. D.

Bordia

Plewa

(2014). Career adaptation: The relation of adaptability to goal orientation, proactive personality, and career optimism. Journal of Vocational Behavior, 84(1), 39–48. https://doi.org/10.1016/j.jvb.2013.11.004

42.

Tomul

(2007). Türkiye’de eğitime katılım üzerinde gelirin etkisi [Effects of household income on participation in education in Turkey]. Pamukkale Üniversitesi Eğitim Fakültesi Dergisi, 2(22), 122–131.

43.

Turkish Statistical Institute (2019). İşgücü istatistikleri, ağustos 2019 [labor statistics. http://www.tuik.gov.tr/PreHaberBultenleri.do?id=24630

44.

Ulaş

(2016). Kariyer kararı verme öz-yetkinliği: Bir model testi [career decision-making self-efficacy: A model testing]. [Unpublished PhD thesis, Hacettepe University.

45.

Vurucu

(2010). Meslek lisesi öğrencilerinin meslek seçimi yeterliği ve meslek seçimini etkileyen faktörler [Profession selection competence of vocational high school students and factors affecting their choice of profession] [Unpublished master thesis. : Yeditepe University.

46.

Weston

R.,

Gore

P. A.

(2006). A brief guide to structural equation modeling. The Counseling Psychologist, 34(5), 719–751. https://doi.org/10.1177%2F0011000006286345

47.

Wheaton

Muthen

Alwin

D. F.

Summers

G. F.

(1977). Assessing reliability and stability in panel models. Sociological Methodology, 8(1), 84–136. https://doi.org/10.2307/270754

48.

Zhuang

She

Cai

Huang

Xiang

Wang

Zhu

(2018). Examining a sequential mediation model of Chinese university students' well-being: A career construction perspective. Frontiers in Psychology, 9, 593. https://doi.org/10.3389/fpsyg.2018.00593